Computational & quantum chemistry

Abstract We present a new version of the nast software package for predicting the kinetics of unimolecular spin-dependent processes, including intersystem crossings, spin-forbidden reactions, and spin crossovers with nonadiabatic statistical theory. With substantial extensions and upgrades, the new nast 2.0 release enables the calculations of (1) rate constants for spin-dependent reactions in an external magnetic field, (2) Marcus–Levich–Jortner rate constants with a new mlj program for complex open-shell systems, (3) Huang–Rhys factors and reorganization energies with a new vib tool, (4) Wigner and Eckart quantum tunneling correction coefficients, (5) minimum energy crossing points with a new pymecp program interfaced with the orca electronic structure package, (6) effective Hessian with the q-chem and orca packages, (7) intrinsic reaction coordinate fits with the orca package for more advanced nast calculations with the Zhu–Nakamura transition probabilities, and (8) transition probabilities and rate constants with integration step-size control. Together, these capabilities significantly enhance the applicability of nast for studying the kinetics of spin-dependent processes even in complex open-shell systems. This paper describes the objectives and applications of the new capabilities illustrated with numerical examples. nast is an open-source software package and is available free of charge.

We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIPs) called MolCryst-MLIPs. The first release comprises fine-tuned MACE models for nine molecular crystal systems─Benzamide, Benzoic acid, Coumarin, Durene, Isonicotinamide, Nicotinic acid, Niacinamide, Pyrazinamide, and Resorcinol─developed using the Automated Machine Learning Pipeline (AMLP), which streamlines the entire MLIP development workflow, from reference data generation to model training and validation, into a reproducible and user-friendly pipeline. Models are fine-tuned from the MACE-MH-1 foundation model (omol head), yielding a mean energy MAE of 0.141 kJ·mol-1·atom-1 and a mean force MAE of 0.648 kJ·mol-1·Å-1 across all systems. Benchmarked against three state-of-the-art foundation models on the DFT-labeled polymorph set, only the fine-tuned models reliably identify the most stable polymorph, with a mean Kendall τ = 0.397 indicating a moderate rank correlation with the full DFT landscape. Dynamical stability and structural integrity, as assessed through energy conservation, P2 orientational order parameters, and radial distribution functions, are evaluated using molecular dynamics simulations. The released models and data sets constitute a growing open database of validated MLIPs, ready for production MD simulations of molecular crystal polymorphism across the polymorphic landscape of each target compound under different thermodynamic conditions.

Accurate solvation free energies from molecular dynamics simulations require efficient sampling of coupled slow variables, including solvent coordinates, solute conformational modes, and the alchemical coordinate $λ$. Here, we develop a $λ$-dynamics framework that combines mass scaling, on-the-fly probability enhanced sampling (OPES), and driven adiabatic free energy dynamics (d-AFED) to address these sampling challenges within a unified protocol. For rigid organic solutes, Hamiltonian replica exchange with mass scaling is first used to quantify the effect of octanol solvent relaxation. Reducing all octanol atomic masses by a factor of ten accelerates convergence by more than fivefold while preserving equilibrium solvation free energies. These calculations then provide reference benchmarks for $λ$-OPES, a dual-bias $λ$-dynamics strategy that combines the "standard" and "explore" variants of OPES to promote transitions along the alchemical coordinate. This approach reaches convergence on timescales comparable to replica exchange, but without predefined $λ$ windows or multiple parallel simulations. For flexible $N$-acetyl amino-acid amide solutes, $λ$-OPES is coupled with d-AFED on selected backbone and side-chain dihedrals to enable simultaneous alchemical and conformational enhanced sampling. This combined strategy improves agreement with experimental octanol-water partition coefficients and reduces the mean absolute error from 0.75 log units with $λ$-OPES alone to 0.30 log units with $λ$-OPES-d-AFED. Overall, this work establishes an integrated enhanced sampling protocol for solvation free energy calculations across rigid organic solutes and flexible peptide-like solutes, and provides a foundation for the application of alchemical free energy methods to larger and more conformationally complex systems.

The stabilization and functional integration of organic radicals within crystalline frameworks remain a significant challenge in materials chemistry, particularly for applications in optoelectronics and sensing. Herein, we report the design and synthesis of a radical-rich, polyrotaxanated covalent organic framework (VCB COF) featuring viologen-based radical cation dimers mechanically interlocked within cucurbit[8]uril (CB[8]) macrocycles. This supramolecular architecture gives rise to a double-walled framework that significantly enhances radical stability, photophysical properties, and photoresponsivity. Electron paramagnetic resonance (EPR) analysis reveals that VCB retains over 72% of its radical signal after 7 days of air exposure, compared to just 22% in its nonrotaxanated analogue, TpV COF. Photophysical studies reveal a 3-fold increase in photoluminescence intensity, while photoelectrochemical measurements demonstrate a stable photocurrent density of 1.3 μA cm-2 under 405 nm illumination with reversible on/off switching. Molecular dynamics simulations together with solid-state NMR support the localization of CB[8] at COF junctions and are consistent with the formation of a double-walled architecture stabilized by dipolar and long-range noncovalent interactions. This work establishes a generalizable strategy for integrating persistent radicals within COFs through macrocycle-assisted encapsulation and mechanical bonding, opening new avenues for the development of robust photoactive, redox-active, and spin-functional materials.

We introduce a computationally simple algorithm for sampling open-chain distributions within the framework of imaginary-time Feynman path integration. The present method is based on the staging algorithm introduced by Pollock and Ceperley [Phys. Rev. B 30, 2555 (1984)] originally developed for computing position-dependent observables. Here, we sample off-diagonal elements of the density matrix, formulated as a distribution describing a linear polymer-like chain of beads, each connected via nearest-neighbor springs to calculate momentum-dependent quantities. This is achieved using a Monte Carlo scheme that ensures efficient and unbiased sampling of all beads along the chain from the free-particle distribution via a staging transformation; we refer to this approach as staging open path integral Monte Carlo (OPIMC). The proposed algorithm is straightforward to implement, as it only involves sampling Gaussian distributions through a transformation defined by a set of recursion relations, followed by a standard Metropolis acceptance/rejection step. The staging OPIMC method accurately reproduces end-to-end and momentum distributions for quantum systems ranging from coupled harmonic oscillators to liquid water.

Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and polarizabilities inaccessible. We introduce DenSNet, a density-first approach to machine-learned electronic structure that learns the Hohenberg--Kohn map from nuclear configurations to the ground-state electron density. Our approach employs an SE(3)-equivariant neural network to predict density coefficients of a flexible atom-centered Gaussian basis, combined with a $Δ$-learning strategy that uses superposed atomic densities as a prior to accelerate training. A second equivariant network then maps the predicted density to the total energy, providing a unified framework for molecular dynamics and electronic structure. We validate DenSNet on ethanol, ethanethiol, and resorcinol, where infrared spectra from machine-learned trajectories show excellent agreement with experimental gas-phase measurements. To test scalability, we train on polythiophene oligomers with 1--6 monomers and extrapolate to chains of up to 12 monomers, generating stable long-time trajectories whose infrared spectra agree with reference density functional theory calculations. Here, we show that reinstating the electron density as the central learned quantity opens a practical route to transferable prediction of spectroscopic and electronic observables in large-scale molecular simulations.

We investigate a coherently driven exciton–photon microcavity with Kerr nonlinearity in the ultrastrong-coupling regime. When the lower and upper polariton branches are well separated in energy, the full Hopfield–Rabi–Kerr model reduces to an effective single-mode description of the lower polariton. We analyze the stability of the lower-polariton steady states. We show that the resulting bistability is qualitatively similar to that in the strong-coupling regime. However, in the ultrastrong-coupling regime counter-rotating processes and the diamagnetic A 2 term renormalize the polariton spectrum and composition, changing the effective detuning Δ ̃ 1 and nonlinearity U LP g-dependency beyond the strong-coupling (RWA) picture. As a result, although the semiclassical bistability criterion keeps its standard Kerr-oscillator form, the turning points and hysteresis window are shifted relative to the strong-coupling prediction.

Concentrated hydrogen-bonded electrolytes (CoHBEs) are structured, electrochemically stable, less-volatile alternatives to aqueous and dilute nonaqueous electrolytes, however, with high viscosities that limit molecular diffusion. This work provides an understanding of the proton conduction mechanism in CoHBEs based on mixtures of acids and azoles and establishes a link between the structurally dictated transport properties and the proton-coupled electron transfer (PCET) reaction rates that can be leveraged for enhancing electrochemical reactions. Diffusion and relaxation NMR studies suggest a breaking of the viscosity–conductivity tradeoff, where at high azole concentrations (>45 mol%), Grotthuss transport is more likely with lowered proton transfer energy barriers between the azole and the acid according to the machine learning (ML) accelerated ab initio path integral MD (AI-PIMD) simulations. Proton conduction pathways are found to be switchable between the hydrogen bonding networks of the acid and the azole, with imidazole chain forming structures better facilitating Grotthuss hopping. Supported by small-angle neutron scattering studies, the chains are found to have six member molecules on average with maximum of 3 to 4 imidazole/imidazoliums at 50 to 60 mol%. Despite their high viscosities, the measured PCET rates for quinones and phenazines measured in the protic CoHBEs present relatively high electron transfer rate constants (k 0 ~ 10 − 4 cm/s), validated by rotating disc electrode and scanning electrochemical microscopy measurements. The results demonstrate that strategic tuning of hydrogen-bond donor–acceptor interactions enables the decoupling of proton transport and viscosity, thereby impacting PCET reactions.

Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computational cost. Yet, developing reliable MLIPs remains difficult because it requires generating high-quality data sets, preprocessing atomic structures, and carefully training and validating models. In this work, we introduce an Automated Machine Learning Pipeline (AMLP) that unifies the entire workflow from data set creation to model validation. AMLP employs large-language-model agents to assist with electronic-structure code selection, input preparation, and output conversion, while its analysis suite (AMLP-Analysis) based on ASE supports a range of molecular simulations. The pipeline is built on the MACE architecture and validated on acridine polymorphs, where with a straightforward fine-tuning of a foundation model mean absolute errors of 1.7 meV/atom in energies and 7.0 meV/Å in forces are achieved. The fitted MLIP reproduces DFT geometries with sub-Å accuracy and demonstrates stability during molecular dynamics simulations in the microcanonical and canonical ensemble.

Transition metal nitrides are reactive intermediates in biological and industrial processes. Chemists have synthesized molecular model complexes of such reactive species to understand their function and electronic requirements for new applications. However, molecular chemistry can suffer from intra- and intermolecular decomposition pathways, which preclude further discovery of unknown reactive species. Metal–organic frameworks offer an opportunity for creating long-lived forms of such species with the vacuum of the pore suppressing degradation while simultaneously enabling substrate access for controlled reactivity studies. Here, we report the characterization of an elusive terminal cobalt nitride species generated through photolysis or thermolysis of a site-isolated cobalt azide within the evacuated metal–organic framework CoN 3-MFU-4 l. The first crystal structure of such a species is presented, with vibrational, X-ray absorption, and electron paramagnetic resonance spectroscopies providing further direct evidence for its formation while elucidating its electronic structure. The system additionally enables subsequent reactivity studies with selected substrates, revealing a unique ambiphilic behavior for a metal nitride species.

Sticky-ended cohesion has been the driving force for DNA self-assembly and has enabled model and design programmability in DNA nanotechnology for over 40 years. Traditional units within self-assembled crystals have rationally-designed sticky ends to avoid unpredictable packing behavior, but in doing so, the surprising variety of contact flavors available to natural nucleic acids remains unexploited. Here, we employ composable DNA tiles to form complex 3D architectures using blunt-ended motifs with single duplex interfaces, leveraging the geometry of the tile and the terminal nucleobase identity to control self-assembly outcomes. We establish programmability and tunable packing, obtaining both unusual and fundamental assemblies, including translational and inversion symmetries at the blunt ends, both 5'-3' and 5'-5' stacking, and both positive and negative twist values. These motifs self-assemble into seven crystallographic space groups, achieving diffraction from 1.86 to 10.0 Å. Finally, we establish the ability of racemic mixtures of L- and D- DNA motifs to perform programmable co-assembly driven by terminal π–π interactions, demonstrating molecular recognition and coexistence between mirror molecular systems. In sum, we demonstrate the ability to tune and program supramolecular assemblies of DNA relying solely on sequence-driven aromatic stacking and tile shape, obviating the need for hydrogen bond recognition between subunits.

Doping-induced superconductivity in group-IV elements may enable quantum functionalities in material systems accessible with well-established semiconductor technologies. Non-equilibrium hyperdoping of group-III atoms into C, Si or Ge can yield superconductivity; however, its origin is obscured by structural disorder and dopant clustering. Here we report the epitaxial growth of hyperdoped Ga:Ge films and trilayer heterostructures by molecular-beam epitaxy with extreme hole concentrations (nh = 4.15 × 1021 cm−3, 17.9% Ga substitution) that yield superconductivity with a critical temperature of Tc = 3.5 K. Synchrotron-based X-ray absorption and scattering methods reveal that Ga dopants are substitutionally incorporated within the Ge lattice, introducing a tetragonal distortion to the crystal unit cell. Our findings, corroborated by first-principles calculations, suggest that the structural order of Ga dopants creates a narrow band for the emergence of superconductivity in Ge, establishing hyperdoped Ga:Ge as a low-disorder, epitaxial superconductor–semiconductor platform. The epitaxial growth of hyperdoped Ga:Ge films and trilayer heterostructures by molecular-beam epitaxy yield superconductivity with a critical temperature of 3.5 K and may enable quantum functionalities in this material system, which is accessible with well-established semiconductor technologies.

Understanding whether a polaritonic phenomenon is fundamentally quantum or classical is essential for building accurate theoretical models and guiding experimental design. Here, we address this question in the context of polaritonic spectra and report an intriguing new feature: the twin polariton, an additional splitting beyond the primary resonant polariton splitting originating from vacuum field fluctuations. We show that the twin polariton persists in the many-molecule limit under permutationally symmetrical initial-state constraint and that it follows the same linear dependence on coupling strength as the primary polariton splitting. This establishes a novel mechanism by which a quantum feature (the twin polariton) can be tuned through a classical one (the primary polariton), offering new opportunities to probe and control the fundamental nature of polaritonic systems.

quantum electrodynamics (QED) has emerged as a compelling extension of quantum chemistry that treats electronic and photonic degrees of freedom on equal footing. In this Perspective, we review the growing landscape of many-body QED methods, including Hartree-Fock, density functional theory (QEDFT), time-dependent DFT (QED-TDDFT), configuration interaction (QED-CI), complete active space (QED-CASSCF), coupled cluster (QED-CC), quantum Monte Carlo (QED-QMC), and density matrix renormalization group (QED-DMRG), highlighting recent developments and implementations. We further explore real-time methods, gradient and Hessian formalisms, and the integration of nonadiabatic nuclear dynamics. Applications range from benchmark simulations of polaritonic chemistry to quantum simulations on emerging quantum hardware. We conclude by outlining future directions for theory development and interdisciplinary efforts at the interface of quantum chemistry, condensed matter, and quantum optics.

Hydrogen bonded electrolytes that exhibit accelerated proton transport via sequential reactive hops have drawn interest for their promise in clean energy applications. Molecular dynamics simulations of these electrolytes offer the opportunity to uncover microscopic mechanistic details that could be used to design and tune the properties of candidate electrolyte technologies. However, accurately modeling the proton transfer reactions and transport properties that give rise to high charge conductivites in these electrolytes proves computationally challenging because of the need to perform lengthy condensed phase simulations, treating both the electronic and nuclear degrees of freedom quantum mechanically. In this paper, we demonstrate that such a modeling task can be efficiently achieved with the use of density functional theory (DFT)-trained machine learning potentials (MLP) to accelerate path integral molecular dynamics (PIMD) simulations. We highlight the practical utility of this approach by using it to benchmark how closely PIMD simulations employing different DFT exchange-correlation functionals reproduce the composition-dependent densities, diffusion coefficients, and electrical conductivities of mixtures consisting of imidazole and levulinic acid. Even with the speedup afforded by our MLPs, PIMD simulations remain quite expensive. In order to render PIMD more computationally tractable, we introduce and benchmark the accuracy of a ring polymer contraction approach that leverages a computationally efficient short-range MLP to accelerate our PIMD simulations by an additional factor of four.

Abstract Recent advances in ion mobility spectrometry have enabled the measurement of rotationally averaged collisional cross-sectional area (CCS) for millions of peptides, as part of routine proteomic mass spectrometry workflows. One of the most striking finding in recent large ion mobility datasets is that CCS exhibits two distinct modes, most notably for charge 3 + peptides. Here, using classical machine learning approaches, we identify that basic site positioning is a key sequence feature determining if a peptide belongs to the high or low CCS mode. Molecular dynamics simulations suggest that peptides in the high CCS mode tend to adopt more extended conformations and form charge-stabilized helical structures, whereas those in the low CCS mode adopt more compact, globular conformations. Further supporting this structural hypothesis, we provide evidence for preferential protonation near the C-terminus, and uncover multiple position-dependent sequence determinants that all suggest the predominance of helix formation in the high mode. Together, these findings will enable better integration of CCS measurements into protein identification and quantification pipelines, improving the performance of ion mobility-based proteomics.

ABT-333 and ABT-072 are two potent non-nucleoside NS5B polymerase inhibitors designed for the treatment of the hepatitis C virus (HCV). These structural analogs differ only by a minor substituent change, which disrupts the planarity of the naphthyl group on the ABT-333 compound through the addition of a more flexible trans-olefin substituent. However, this minor change leads to significant differences in their conformational preferences and intermolecular interactions, resulting in a ripple effect with drug development implications, ranging from crystal polymorphism and low aqueous solubility to formulation development challenges. In this article, we demonstrate how a suite of molecular simulation approaches, including crystal structure prediction augmented with a new hydrate CSP algorithm, free-energy perturbation, molecular dynamics (MD) based solubility predictions, and topological assessment to evaluate surface re-crystallization tendencies, provide key atomistic-level insights into the differentiated performance of the two analogs. Through this study, we establish the importance of end-to-end physics-based modeling, which involves explicit considerations of 3-D structure and crystal packing interactions. This approach provides structural and energetic insights into the physicochemical properties and drug development challenges faced when designing best-in-class drug molecules. ABT-333 and ABT-072 are non-nucleoside NS5B polymerase inhibitors for hepatitis C, differing by a minor substituent change that impacts their drug development profiles. Here, the authors use advanced molecular simulations, including crystal structure prediction augmented with a new hydrate CSP algorithm and advanced MD simulations, to reveal how this change affects conformational preferences and interactions, highlighting the importance of physics-based modeling in drug design.

An approach is introduced to reduce the computational cost associated with performing path integral simulations. This work is an extension of the ring-polymer contraction (RPC) approach of Markland and Manolopoulos [J. Chem. Phys. 129, 024105 (2008)], which was originally formulated for simulating closed ring-polymers via molecular dynamics using normal modes to transform the full ring into a contracted ring. This work considers several new contraction transformation schemes, specifically a transformation to the contracted ring employing staging variables, designated as staging RPC, and a transformation for open-chain path integrals used in the calculation of momentum-dependent properties and quantum time correlation functions, which we designate as staging open-polymer contraction (OPC). These approaches are shown to be useful for obtaining equilibrium and dynamical properties for systems ranging from one-dimensional model problems to three-dimensional gas-phase water clusters. Additionally, we show that the accuracy and efficiency of contraction approaches can be improved significantly via a simple reweighting scheme. The advantage of staging RPC/OPC over normal mode RPC is the ability to formulate all transformations as a set of a recursion relations rather than matrix multiplications or Fourier transforms, providing a computationally simpler approach that is as effective as normal mode based contraction.

Chiral magnetic textures are of fundamental interest for studying exotic spin behavior, but also promising for the development of energy-efficient memory and logic devices. Recent computational work has suggested that in contrast to well-studied static chiral textures like skyrmions, there exists dynamic fluctuation-driven magnetic chirality even in the absence of long-range magnetic order. Here, we present definitive magnetotransport signatures of fluctuation-driven chirality in a model material system of Pt/Li_{0.5}Al_{1.0}Fe_{1.5}O_{4} (LAFO) bilayers, where LAFO is a ferromagnetic insulator. We establish a fluctuation-driven origin for the magnetic chirality by observing a strong correlation between the onset of the topological Hall effect and the destruction of magnetic order. Corroborated by Monte Carlo simulations, we develop a rigorous analytical framework to gain a first-principles understanding of scaling behaviors in the transport data. Our results bring novel insights to fluctuation-driven chirality and analysis of magnetotransport data.

High Resolution Image Download MS PowerPoint Slide Understanding the impact of O 2 during a carbon capture process is vital for designing robust, cost-effective materials for carrying it out. However, mechanistic studies of the O 2-induced degradation of materials are not easily undertaken owing to the complex sequential reaction pathways that arise. Here, we report comprehensive mechanistic investigations of the O 2-induced degradation of diamine-appended metal–organic frameworks (MOFs) exhibiting cooperative CO 2 adsorption. Oxygen exposure experiments were performed on seven different diamine-appended MOFs, including e-2–Mg 2 (dobpdc) (e-2 = N-ethylethylenediamine, dobpdc 4– = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate), under various temperatures and O 2 pressures. These experiments show that diamine degradation inhibits CO 2 chemisorption and that the degradation rate is significantly influenced by the diamine structure. In contrast, the parent frameworks remain essentially intact upon O 2 exposure. Detailed characterization of O 2-exposed e-2–Mg 2 (dobpdc) revealed the formation of various degradation products, including acetaldehyde, carbon dioxide, water, ethylamine, and other aldehyde- and imine-containing species. Together, these observations suggest that diamine degradation occurs via C–N bond cleavage through pathways involving C-centered radicals. Furthermore, computational evaluation of the initiation and propagation pathways for amine degradation in diamine-appended MOFs indicates that (i) degradation is likely initiated by OH •, (ii) carbon-centered radicals generated via radical transfer reactions react with O 2, leading to amine degradation, and (iii) the rate-limiting step of the degradation reactions likely involves O–O bond cleavage. Overall, these mechanistic insights could inform strategies for mitigating O 2-induced amine degradation in next-generation carbon capture technologies.

Three recently reported iron-containing metal–organic frameworks, FeZn 4 (prv) 4 (btdd) 3 (Fe- 1), Fe 1.8 Zn 3.2 (prv) 4 (btdd) 3 (Fe 1.8 - 1), and FeZn 4 (moba) 4 (btdd) 3 (Fe- 2) (Hprv = pyruvic acid, Hmoba = 3,3-dimethyl-2-oxobutanoic acid, and H 2 btdd = bis(1 H-1,2,3-triazolo[4,5- b],[4′,5′- i])dibenzo[1,4]dioxin), that react with O 2 at low temperatures to generate high-spin (S = 2) ferryl species provide an unprecedented opportunity to explore the effects of spin state and site-isolation on hydrocarbon oxidation reaction mechanisms. These reagents oxidize the radical clock substrate norcarane, yielding radical lifetimes in the low nanosecond regime, consistent with a diffusion-limited rebound mechanism. Rebound products derived from ligands on the iron centers inside the framework are detected and recapitulate chemistry seen in the structurally related α-ketoglutarate-dependent dioxygenases such as TauD, which also generate high-spin (S = 2) ferryl intermediates. Framework models indicate that productive reactions occur when norcarane approaches the ferryl species from the larger of the two pores of the MFU-4 l-type framework.

Collective variable (CV) and generalized ensemble-based enhanced sampling methods are widely used for accelerating barrier-crossing events and enhancing conformational sampling in molecular dynamics simulations. Temperature-accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED) uses extended variables thermostated at high temperature to achieve better exploration of conformational space. Replica exchange with solute tempering (REST2) achieves improved sampling by scaling the solute-solute and solute-solvent interaction energies of different replicas and swapping conformations between them. It has been observed that a combination of CV-based enhanced sampling and global tempering is needed to boost the conformational sampling of large biomolecular systems due to the presence of large entropic basins. In this work, we propose a method called "Solute Tempered d-AFED" or "STed-AFED" that combines both d-AFED/TAMD and REST2. We implemented this approach in the OpenMM-UFEDMM interface and demonstrated the efficiency of this method by studying the conformational landscapes of small peptides and proteins, in particular, chignolin, Trp-cage, and villin.

High Resolution Image Download MS PowerPoint Slide Nickel catalysis enables cross-coupling of a broad scope of C(sp 3) moieties by mediating carbon–carbon bond formation from carbon-centered radicals. A widely proposed mechanism involves stepwise radical capture by a nickel(II) complex that forms a nickel(III) intermediate. The alternative pathway, a concerted radical capture and carbon–carbon bond formation, has been largely overlooked. This study investigates the ligand effect and kinetics of nickel-mediated radical capture and reductive elimination, which provide evidence to distinguish between stepwise and concerted pathways. Through radical clock experiments, spectroscopic investigation, electrochemical studies, and multivariate linear regression analysis of a series of [(pybox)Ni(Ar)]BAr F 4 complexes, we established a strong correlation between the rate of radical capture and HOMO and LUMO energies, along with positive charge stabilization at nickel and the aryl actor ligand. These data rule out the stepwise formation of a nickel(III) intermediate and support a concerted pathway. Redox-active nitrogen ligands and nonredox-active phosphine ligands exhibit contrasting reactivity, with only redox-active ligands facilitating radical capture and carbon–carbon bond formation. This critical role of ligand redox activity can be attributed to the participation of the LUMO in bond cleavage and formation. Among redox-active ligands, bidentate and tridentate ligands exhibit similar rates, suggesting a consistent mechanism with relatively minimal ancillary ligand effect. Our results highlight the critical interplay between ligand electronics, sterics, and orbital contributions, offering valuable design principles for nickel-catalyzed cross-coupling reactions involving radical intermediates.

Accurate modeling of the dynamic structures of ribonucleic acid (RNA) molecules is essential for understanding their biological roles. However, such modeling remains challenging due to limitations in current force fields. This study critically evaluates three RNA force fields, HB-CUFIX, AMBER-χOL3, and AMBER-ROC, comparing their performance against experimental nuclear magnetic resonance and small-angle x-ray scattering data for single-stranded oligonucleotides. Using enhanced sampling techniques, specifically Unified Free Energy Dynamics, we exhaustively sampled the conformational space of tetramer and hexamer RNA sequences, achieving a detailed and thermodynamically converged view of their structural dynamics. Our findings reveal that HB-CUFIX outperforms AMBER-χOL3 and AMBER-ROC, providing near-experimental accuracy in capturing sequence-dependent structural preferences. In particular, HB-CUFIX accurately predicts low energy states for the AAAA and CCCC sequences, favoring A-form helical conformations, while the UUUU sequence adopts an extended, heterogeneous structure. The mixed GACC sequence displays a predominantly A-form helix with flexible terminal residues. These results highlight the significant role of sequence in dictating RNA conformational spaces, which are driven by base stacking interactions and covalent geometry. We also emphasize the importance of enhanced sampling, particularly methods that can handle large numbers of collective variables, in evaluating RNA force fields, as traditional brute-force Molecular dynamics fails to capture the conformational diversity of flexible RNAs. Our study provides a reliable tool for RNA structure prediction and dynamic analysis, supporting future advancements in RNA-targeted research and therapeutic design.

Understanding the impact of O2 during a carbon capture process is vital for designing robust, cost-effective materials for carrying it out. However, mechanistic studies of the O2-induced degradation of materials are not easily undertaken owing to the complex sequential reaction pathways that arise. Here, we report comprehensive mechanistic investigations of the O2-induced degradation of diamine-appended metal–organic frameworks (MOFs) exhibiting cooperative CO2 adsorption. Oxygen exposure experiments were performed on seven different diamine-appended MOFs, includ-ing e-2–Mg2(dobpdc) (e-2 = N-ethylethylenediamine, dobpdc4− = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate), under various temperatures and O2 pressures. These experiments show that diamine degradation inhibits CO2 chemisorption, and that the degradation rate is significantly influenced by the diamine structure. In contrast, the parent frame-works remain essentially intact upon O2 exposure. Detailed characterization of O2-exposed e-2–Mg2(dobpdc) revealed the formation of various degradation products, including acetaldehyde, carbon dioxide, water, ethylamine, and other aldehyde- and imine-containing species. Together, these observations suggest that diamine degradation occurs via C–N bond cleavage through pathways involving C-centered radicals. Furthermore, computational evaluation of the initiation and propagation pathways for amine degradation in diamine-appended MOFs indicates that: (i) degradation is likely initiated by OH•, (ii) carbon-centered radicals generated via radical transfer reactions react with O2, leading to amine degradation, and (iii) the rate-limiting step of the degradation reactions likely involves O–O bond cleavage. Overall, these mechanistic insights could inform strategies for mitigating O2-induced amine degradation in next-generation carbon capture technologies.

A force field as accurate as quantum mechanics (QMs) and as fast as molecular mechanics (MMs), with which one can simulate a biomolecular system efficiently enough and meaningfully enough to get quantitative insights, is among the most ardent dreams of biophysicists—a dream, nevertheless, not to be fulfilled any time soon. Machine learning force fields (MLFFs) represent a meaningful endeavor in this direction, where differentiable neural functions are parametrized to fit ab initio energies and forces through automatic differentiation. We argue that, as of now, the utility of the MLFF models is no longer bottlenecked by accuracy but primarily by their speed, as well as stability and generalizability—many recent variants, on limited chemical spaces, have long surpassed the chemical accuracy of 1 kcal/mol—the empirical threshold beyond which realistic chemical predictions are possible—though still magnitudes slower than MM. Hoping to kindle exploration and design of faster, albeit perhaps slightly less accurate MLFFs, in this review, we focus our attention on the technical design space (the speed-accuracy trade-off) between MM and ML force fields. After a brief review of the building blocks (from a machine learning-centric point of view) of force fields of either kind, we discuss the desired properties and challenges now faced by the force field development community, survey the efforts to make MM force fields more accurate and ML force fields faster, and envision what the next generation of MLFF might look like.

High Resolution Image Download MS PowerPoint Slide Room-temperature ionic liquids (RTILs) represent a versatile class of chemical systems composed entirely of oppositely charged species whose bulk properties can be fine-tuned by adjusting molecular structures and, consequently, intermolecular interactions. Understanding the intricate dynamics between ionic species can aid in the rational design of RTILs for specific applications in a range of fields, including catalysis and electrochemistry. Here, we investigate the temperature dependence of intermolecular interactions through magnetization transfer by means of 1 H– 19 F heteronuclear Overhauser effect spectroscopy (HOESY) for two ionic liquids, namely, [BMIM][BF 4] and [BMIM][PF 6]. We find that the cross-relaxation rates vary significantly over a rather small temperature range, even changing sign. Molecular dynamics (MD) simulations on neat RTIL systems replicate this behavior well and further show that the dynamic properties rather than coordination changes of RTILs account for the observed temperature behavior. Furthermore, the investigation of different coordination shells highlights the change of interaction range with temperature even to the point where inner and outer coordination shells could be in distinct motional regimes with cross-relaxation rates of opposite sign. Since temperature changes lead primarily to dynamic changes rather than structural ones, these findings underscore the versatility and high thermal stability of ionic liquids.

Structural, thermal, and dynamic properties of four deep eutectic solvents comprising choline chloride paired with ortho- phenolic derivative hydrogen-bond donors were probed using experiments and molecular simulations. The hydrogen-bond donors include phenol, catechol, o-chlorophenol, and o-cresol, in a 3:1 mixture with the hydrogen-bond acceptor choline chloride. Density, viscosity, and pulsed-field gradient NMR diffusivity measurements were conducted over a range of temperatures. Classical and ab initio molecular dynamics simulation results match experimental data reasonably well. The simulation results were then used to perform a more detailed analysis of the local structure and dynamics of these systems.

Single molecules bridging two metallic electrodes can emit light through electroluminescence when subjected to a bias voltage. Typically, light emission in such devices results from transitions between molecular states, although in the presence of light-matter coupling, the emission can result from a transition between hybrid light-matter states. Here, we create single metal-molecule-metal junctions and simultaneously collect conductance and electroluminescence data using a scanning tunneling microscope (STM) equipped with a custom spectrometer. Through experimental analysis and electronic structure calculations, we provide evidence for a molecule-electrode interfacial exciton coupled to a junction cavity plasmon. Importantly, we find that close to resonant transport conditions, the molecular junction functions as a single emitter that is strongly coupled to the junction cavity mode, leading to characteristic Rabi splitting of the emission spectrum and providing the first example of an electroluminescence-driven single-molecule system in the regime of strong light-matter coupling.

Model stealing, where a learner tries to recover an unknown model via carefully chosen queries, is a critical problem in machine learning, as it threatens the security of proprietary models and the privacy of data they are trained on. In recent years, there has been particular interest in stealing large language models (LLMs). In this paper, we aim to build a theoretical understanding of stealing language models by studying a simple and mathematically tractable setting. We study model stealing for Hidden Markov Models (HMMs), and more generally low-rank language models. We assume that the learner works in the conditional query model, introduced by Kakade, Krishnamurthy, Mahajan and Zhang. Our main result is an efficient algorithm in the conditional query model, for learning any low-rank distribution. In other words, our algorithm succeeds at stealing any language model whose output distribution is low-rank. This improves upon the previous result by Kakade, Krishnamurthy, Mahajan and Zhang, which also requires the unknown distribution to have high "fidelity", a property that holds only in restricted cases. There are two key insights behind our algorithm: First, we represent the conditional distributions at each timestep by constructing barycentric spanners among a collection of vectors of exponentially large dimension. Second, for sampling from our representation, we iteratively solve a sequence of convex optimization problems that involve projection in relative entropy to prevent compounding of errors over the length of the sequence. This is an interesting example where, at least theoretically, allowing a machine learning model to solve more complex problems at inference time can lead to drastic improvements in its performance.

High Resolution Image Download MS PowerPoint Slide Dynamic or structurally induced ionization is a critical aspect of many physical, chemical, and biological processes. Molecular dynamics (MD) based simulation approaches, specifically constant pH MD methods, have been developed to simulate ionization states of molecules or proteins under experimentally or physiologically relevant conditions. While such approaches are now widely utilized to predict ionization sites of macromolecules or to study physical or biological phenomena, they are often computationally expensive and require long simulation times to converge. In this article, using the principles of adiabatic free energy dynamics, we introduce an efficient technique for performing constant pH MD simulations within the framework of the adiabatic free energy dynamics (AFED) approach. We call the new approach pH-AFED. We show that pH-AFED provides highly accurate predictions of protein residue p K a values, with a MUE of 0.5 p K a units when coupled with driven adiabatic free energy dynamics (d-AFED), while reducing the required simulation times by more than an order of magnitude. In addition, pH-AFED can be easily integrated into most constant pH MD codes or implementations and flexibly adapted to work in conjunction with enhanced sampling algorithms that target collective variables. We demonstrate that our approaches, with both pH-AFED standalone as well as pH-AFED combined with collective variable based enhanced sampling, provide promising predictive accuracy, with a MUE of 0.6 and 0.5 p K a units respectively, on a diverse range of proteins and enzymes, ranging up to 186 residues and 21 titratable sites. Lastly, we demonstrate how this approach can be utilized to understand the in vivo performance engineered antibodies for immunotherapy.

Abstract There is an increasing demand for the development of ion‐conducting electrolytes for energy storage systems. Much attention is directed toward deep eutectic solvents as potential candidates. In the search for highly conductive systems, the possibility of designing deep eutectic solvents with Grotthuss‐type proton transport is widely overlooked. Herein, ethaline, a mixture of choline chloride and ethylene glycol is used in a 1:2 molar ratio, to induce a significant conductivity increase with the addition of water and sulfuric acid (H2SO4). The achieved breakthrough conductivity is analyzed experimentally and simulated with ab initio molecular dynamics (AIMD). At sufficient water content, an H‐bonding network is formed that leads to a significant breakthrough conductivity based on H2SO4‐derived proton transfer following the long‐established Grotthuss proton transport mechanism. This result is substantiated by the positive deviation from the ideal KCl line in the Walden plot. Specifically, the data series positioned above the reference line indicates a Grotthuss mechanism in action. The AIMD simulations demonstrate proton transfer between water and ethylene glycol, supported by simulation frames captured at various times.

High Resolution Image Download MS PowerPoint Slide Magnesium (Mg 2+) is the most abundant divalent cation in the cell and is essential to nearly every biochemical reaction involving adenosine triphosphate (ATP) and its lower energy counterpart, adenosine diphosphate (ADP). In this work, we examine the solution dynamics of ADP at different concentrations and record the changes thereof due to the presence of Mg 2+ ions. Relaxation and diffusion experiments were performed on a range of ADP solutions with increasing magnesium concentration. The most significant changes of both relaxation and diffusion behaviors are observed when adding Mg 2+ up to 0.5 ADP equivalent (eq), with most of the changes complete at 1 eq. Molecular dynamics simulations also show a significant structure introduced by Mg 2+ with very stable pyramidal coordination with the phosphate oxygens. A more extended structure found in the presence of Mg 2+ is consistent with the experimental slowing of diffusion and an increase in the spin–lattice relaxation rate. We do not observe direct evidence of aggregation in solution, although translational diffusion is slowed down significantly at higher concentrations (while solvent diffusion remains constant).

The study of electrolytic solutions is of relevance in many research fields, ranging from biophysics, materials, and colloid science to catalysis and electrochemistry. The dependence of solution dynamics on the nature of electrolytes and their concentrations has been the subject of many experimental and computational studies, yet it remains challenging to obtain a full understanding of the factors that govern solution behavior. Here, we provide additional insights into the behavior of aqueous solutions of alkali chlorides by combining 17O relaxation data with diffusion and viscosity data and contrast their behavior with 1H nuclear magnetic resonance relaxation data. The main findings are that 17O relaxation correlates well with viscosity data but not with diffusion data, while 1H relaxation correlates with neither. Certain ionic trends match known ion-specific series behavior, especially at high concentrations. Notably, we also examine the ranges of the interactions and conclude that the majority of the effects are tied to local water reorientation dynamics.

A novel approach to computationally enhance the sampling of molecular crystal structures is proposed and tested. This method is based on the use of extended variables coupled to a Monte Carlo based crystal polymorph generator. Inspired by the established technique of quasi-random sampling of polymorphs using the rigid molecule constraint, this approach represents molecular clusters as extended variables within a thermal reservoir. Polymorph unit-cell variables are generated using pseudo-random sampling. Within this framework, a harmonic coupling between the extended variables and polymorph configurations is established. The extended variables remain fixed during the inner loop dedicated to polymorph sampling, enforcing a stepwise propagation of the extended variables to maintain system exploration. The final processing step results in a polymorph energy landscape, where the raw structures sampled to create the extended variable trajectory are re-optimized without the thermal coupling term. The foundational principles of this approach are described and its effectiveness using both a Metropolis Monte Carlo type algorithm and modifications that incorporate replica exchange is demonstrated. A comparison is provided with pseudo-random sampling of polymorphs for the molecule coumarin. The choice to test a design of this algorithm as relevant for enhanced sampling of crystal structures was due to the obvious relation between molecular structure variables and corresponding crystal polymorphs as representative of the inherent vapor to crystal transitions that exist in nature. Additionally, it is shown that the trajectories of extended variables can be harnessed to extract fluctuation properties that can lead to valuable insights. A novel thermodynamic variable is introduced: the free energy difference between ensembles of Z' = 1 and Z' = 2 crystal polymorphs.

Because of its importance in various aspects of everyday life, silica is a material that has been the subject of extensive research. Studies on its amorphous phase have particularly benefited from the contribution of atomistic simulations to understand the close relationships between its structure and properties. In this context, the main difficulty lies in the compromise that had to be made between the precision of the interactions that need to be computed at an ab initio level and the important statistics required to describe disorder. With the advent of machine learning approaches, it is now possible to couple accuracy and statistics by using interatomic potentials trained on ab initio databases. This opens up unprecedented prospects for studies where calculation accuracy, system size and trajectory length are critical. In this work, we propose a machine learning potential for silica obtained from a neural network trained on a database consisting of a few hundred configurations extracted from an ab initio molecular dynamics trajectory at the Density Functional Theory (DFT) level of a silica liquid at high temperature and under pressure. We show that this potential is sufficiently accurate to describe the liquid and amorphous phases of silica, and that it is also transferable to glasses under moderate pressure and, more surprisingly, to certain crystalline phases.

High Resolution Image Download MS PowerPoint Slide Nanoconfined anion exchange membranes (AEMs) play a vital role in emerging electrochemical technologies. The ability to control dominant hydroxide diffusion pathways is an important goal in the design of nanoconfined AEMs. Such control can shorten hydroxide transport pathways between electrodes, reduce transport resistance, and enhance device performance. In this work, we propose an electrostatic potential (ESP) approach to explore the effect of the polymer electrolyte cation spacing on hydroxide diffusion pathways from a molecular perspective. By exploring cation ESP energy surfaces and validating outcomes through prior ab initio molecular dynamics simulations of nanoconfined AEMs, we find that we can achieve control over preferred hydroxide diffusion pathways by adjusting the cation spacing. The results presented in this work provide a unique and straightforward approach to predict preferential hydroxide diffusion pathways, enabling efficient design of highly conductive nanoconfined AEM materials for electrochemical technologies.

We investigate twisted bilayer graphene (TBG) at filling ν=-3 in the presence of realistic heterostrain. Strain amplifies the band dispersion and drives the system beyond the strong-coupling regime of previous theoretical studies. We use DMRG to conduct an unbiased, large-scale numerical calculations that include all spin and valley degrees of freedom, up to bond dimension χ=240.16em0ex576. We establish a global phase diagram that unifies a number of theoretical and experimental results. Near zero strain we find an intervalley-coherent quantized anomalous Hall (QAH-IVC) state, a competitive strong-coupling order that evaded past numerical studies. A tiny strain around $0.05%$ drives a transition into an incommensurate Kekul\'e spiral (IKS) phase, supporting the mean-field prediction in [Kwan et al., Phys. Rev. X 11, 041063 (2021)]. Even higher strains above $0.2%$ favor a flavor-symmetric metallic order, which may explain metals found at ν=-3 in many experiments.

The dynamic interplay between a copper electrode and a reactive ionic liquid is illustrated in the cover picture, showcasing key components in the electrochemical reduction of CO2. The role of the cation in covering the electrode surface and the in-situ generated hydrogen bond donor from CO2 binding to the ionic liquid in enhancing the kinetics were uncovered by in-situ spectroscopy, complemented by electroanalytical and computational methods. Multi-carbon products with reduced reaction energy were obtained, as reported by Burcu Gurkan et al. in their Research Article (e202312163). Cover image credit: Miguel Munoz.

The dynamic interplay between a copper electrode and a reactive ionic liquid is illustrated in the cover picture, showcasing key components in the electrochemical reduction of CO2. The role of the cation in covering the electrode surface and the in-situ generated hydrogen bond donor from CO2 binding to the ionic liquid in enhancing the kinetics were uncovered by in-situ spectroscopy, complemented by electroanalytical and computational methods. Multi-carbon products with reduced reaction energy were obtained, as reported by Burcu Gurkan et al. in their Research Article (e202312163). Cover image credit: Miguel Munoz.

In nature, nonheme iron enzymes use dioxygen to generate high-spin iron(IV)=O species for a variety of oxygenation reactions. Although synthetic chemists have long sought to mimic this reactivity, the enzyme-like activation of O 2 to form high-spin iron(IV)=O species remains an unrealized goal. Here, we report a metal–organic framework featuring iron(II) sites with a local structure similar to that in α-ketoglutarate-dependent dioxygenases. The framework reacts with O 2 at low temperatures to form high-spin iron(IV)=O species that are characterized using in situ diffuse reflectance infrared Fourier transform, in situ and variable-field Mössbauer, Fe Kβ x-ray emission, and nuclear resonance vibrational spectroscopies. In the presence of O 2, the framework is competent for catalytic oxygenation of cyclohexane and the stoichiometric conversion of ethane to ethanol.

Abstract Electrochemical CO 2 reduction (CO 2 RR) on copper (Cu) shows promise for higher‐value products beyond CO. However, challenges such as the limited CO 2 solubility, high overpotentials, and the competing hydrogen evolution reaction (HER) in aqueous electrolytes hinder the practical realization. We propose a functionalized ionic liquid (IL) which generates ion‐CO 2 adducts and a hydrogen bond donor (HBD) upon CO 2 absorption to modulate CO 2 RR on Cu in a non‐aqueous electrolyte. As revealed by transient voltammetry, electrochemical impedance spectroscopy (EIS), and in situ surface‐enhanced Raman spectroscopy (SERS) complemented with image charge augmented quantum‐mechanical/molecular mechanics (IC‐QM/MM) computations, a unique microenvironment is constructed. In this microenvironment, the catalytic activity is primarily governed by the IL and HBD concentrations; former controlling the double layer thickness and the latter modulating the local proton availability. This translates to ample CO 2 availability, reduced overpotential, and suppressed HER where C 4 products are obtained. This study deepens the understanding of electrolyte effects in CO 2 RR and the role of IL ions towards electrocatalytic microenvironment design.

Abstract Electrochemical CO2 reduction (CO2RR) on copper (Cu) shows promise for higher‐value products beyond CO. However, challenges such as the limited CO2 solubility, high overpotentials, and the competing hydrogen evolution reaction (HER) in aqueous electrolytes hinder the practical realization. We propose a functionalized ionic liquid (IL) which generates ion‐CO2 adducts and a hydrogen bond donor (HBD) upon CO2 absorption to modulate CO2RR on Cu in a non‐aqueous electrolyte. As revealed by transient voltammetry, electrochemical impedance spectroscopy (EIS), and in situ surface‐enhanced Raman spectroscopy (SERS) complemented with image charge augmented quantum‐mechanical/molecular mechanics (IC‐QM/MM) computations, a unique microenvironment is constructed. In this microenvironment, the catalytic activity is primarily governed by the IL and HBD concentrations; former controlling the double layer thickness and the latter modulating the local proton availability. This translates to ample CO2 availability, reduced overpotential, and suppressed HER where C4 products are obtained. This study deepens the understanding of electrolyte effects in CO2RR and the role of IL ions towards electrocatalytic microenvironment design.

The theorems of density functional theory (DFT) establish bijective maps between the local external potential of a many-body system and its electron density, wavefunction and, therefore, one-particle reduced density matrix. Building on this foundation, we show that machine learning models based on the one-electron reduced density matrix can be used to generate surrogate electronic structure methods. We generate surrogates of local and hybrid DFT, Hartree-Fock and full configuration interaction theories for systems ranging from small molecules such as water to more complex compounds like benzene and propanol. The surrogate models use the one-electron reduced density matrix as the central quantity to be learned. From the predicted density matrices, we show that either standard quantum chemistry or a second machine-learning model can be used to compute molecular observables, energies, and atomic forces. The surrogate models can generate essentially anything that a standard electronic structure method can, ranging from band gaps and Kohn-Sham orbitals to energy-conserving ab-initio molecular dynamics simulations and infrared spectra, which account for anharmonicity and thermal effects, without the need to employ computationally expensive algorithms such as self-consistent field theory. The algorithms are packaged in an efficient and easy to use Python code, QMLearn, accessible on popular platforms.

Abstract Complex problems that cross traditional disciplinary lines between physics, chemistry, biology, and materials science can be studied at an unprecedented level of detail using increasingly sophisticated theoretical methodology and high-speed computing platforms. The tools of statistical mechanics provide the bridge between the atomistic descriptions of these complex systems and the macroscopic observables accessible to experimental investigations and predictable in computer simulations. The aim of this book is to prepare burgeoning users and developers to become active researchers in the theoretical and computational molecular sciences by uniting, in one monograph, the theoretical underpinnings of equilibrium and time-dependent classical and quantum statistical mechanics with modern computational techniques used to put these concepts into practice to address real-world applications. The book contains detailed reviews of classical and quantum mechanics and in-depth discussions of the most commonly used statistical ensembles side by side with modern computational methods such as molecular dynamics, Monte Carlo, advanced configurational and trajectory sampling approaches, free-energy based rare-event sampling approaches, Feynman path integral techniques, linear response theory and time correlation functions, stochastic methods, critical phenomena, and an introduction to machine learning and its uses in statistical mechanics. Readers of this book will be provided, in a pedagogical manner, with a firm foundation in both the theory and practical implementation of statistical mechanical concepts, thus allowing them to approach application technology with an understanding of the underlying algorithms and to become, themselves, creators of new and powerful approaches for solving challenging research problems.

In the quest for mathematical foundations of M-theory, the Hypothesis H that fluxes are quantized in Cohomotopy theory, implies, on flat but possibly singular spacetimes, that M-brane charges locally organize into equivariant homotopy groups of spheres. Here, we show how this leads to a correspondence between phenomena conjectured in M-theory and fundamental mathematical concepts/results in stable homotopy, generalized cohomology and Cobordism theory [Formula: see text]: — stems of homotopy groups correspond to charges of probe [Formula: see text]-branes near black [Formula: see text]-branes; — stabilization within a stem is the boundary-bulk transition; — the Adams d-invariant measures [Formula: see text]-flux; — trivialization of the d-invariant corresponds to [Formula: see text]-flux; — refined Toda brackets measure [Formula: see text]-flux; — the refined Adams e-invariant sees the [Formula: see text]-charge lattice; — vanishing Adams e-invariant implies consistent global [Formula: see text]-fields; — Conner–Floyd’s e-invariant is the [Formula: see text]-flux seen in the Green–Schwarz mechanism; — the Hopf invariant is the M2-brane Page charge ([Formula: see text]-flux); — the Pontrjagin–Thom theorem associates the polarized brane worldvolumes sourcing all these charges. In particular, spontaneous K3-reductions with 24 branes are singled out from first principles: — Cobordism in the third stable stem witnesses spontaneous KK-compactification on K3-surfaces; — the order of the third stable stem implies the 24 NS5/D7-branes in M/F-theory on K3. Finally, complex-oriented cohomology emerges from Hypothesis H, connecting it to all previous proposals for brane charge quantization in the chromatic tower: K-theory, elliptic cohomology, etc.: — quaternionic orientations correspond to unit [Formula: see text]-fluxes near M2-branes; — complex orientations lift these unit [Formula: see text]-fluxes to heterotic M-theory with heterotic line bundles. In fact, we find quaternionic/complex Ravenel-orientations bounded in dimension; and we find the bound to be 10, as befits spacetime dimension [Formula: see text].

Collective variable (CV)-based enhanced sampling techniques are widely used today for accelerating barrier-crossing events in molecular simulations. A class of these methods, which includes temperature accelerated molecular dynamics (TAMD)/driven-adiabatic free energy dynamics (d-AFED), unified free energy dynamics (UFED), and temperature accelerated sliced sampling (TASS), uses an extended variable formalism to achieve quick exploration of conformational space. These techniques are powerful, as they enhance the sampling of a large number of CVs simultaneously compared to other techniques. Extended variables are kept at a much higher temperature than the physical temperature by ensuring adiabatic separation between the extended and physical subsystems and employing rigorous thermostatting. In this work, we present a computational platform to perform extended phase space enhanced sampling simulations using the open-source molecular dynamics engine OpenMM. The implementation allows users to have interoperability of sampling techniques, as well as employ state-of-the-art thermostats and multiple time-stepping. This work also presents protocols for determining the critical parameters and procedures for reconstructing high-dimensional free energy surfaces. As a demonstration, we present simulation results on the high dimensional conformational landscapes of the alanine tripeptide in vacuo, tetra-N-methylglycine (tetra-sarcosine) peptoid in implicit solvent, and the Trp-cage mini protein in explicit water.

Determining collective variables (CVs) for conformational transitions is crucial to understanding their dynamics and targeting them in enhanced sampling simulations. Often, CVs are proposed based on intuition or prior knowledge of a system. However, the problem of systematically determining a proper reaction coordinate (RC) for a specific process in terms of a set of putative CVs can be achieved using committor analysis (CA). Identifying essential degrees of freedom that govern such transitions using CA remains elusive because of the high dimensionality of the conformational space. Various schemes exist to leverage the power of machine learning (ML) to extract an RC from CA. Here, we extend these studies and compare the ability of 17 different ML schemes to identify accurate RCs associated with conformational transitions. We tested these methods on an alanine dipeptide in vacuum and on a sarcosine dipeptoid in an implicit solvent. Our comparison revealed that the light gradient boosting machine method outperforms other methods. In order to extract key features from the models, we employed Shapley Additive exPlanations analysis and compared its interpretation with the "feature importance" approach. For the alanine dipeptide, our methodology identifies ϕ and θ dihedrals as essential degrees of freedom in the C7ax to C7eq transition. For the sarcosine dipeptoid system, the dihedrals ψ and ω are the most important for the cisαD to transαD transition. We further argue that analysis of the full dynamical pathway, and not just endpoint states, is essential for identifying key degrees of freedom governing transitions.

Twisted bilayer graphene (TBG) has garnered significant interest in condensed matter physics over the past few years. Here, the authors present numerical investigations of TBG implementing state-of-the-art quantum chemistry methods. Using a gauge-invariant order parameter, they show a C2z\ensuremath{\mathcal{T}} phase transition at charge neutrality which persists at noninteger fillings near charge neutrality. The work is the first systematic study of TBG for noninteger fillings near charge neutrality.

Thermally-induced transitions between bistable magnetic states of magnetic tunnel junctions (MTJ) are of interest for generating random bitstreams and for applications in stochastic computing. An applied field transverse to the easy axis of a perpendicularly magnetized MTJ (pMTJ) can lower the energy barrier (Eb) to these transitions leading to faster fluctuations. In this study, we present analytical and numerical calculations of Eb considering both coherent (macrospin) reversal and non-uniform wall-mediated magnetization reversal for a selection of nanodisk diameters and applied fields. Non-uniform reversal processes dominate for larger diameters, and our numerical calculations of Eb using the String method show that the transition state has a sigmoidal magnetization profile. The latter can be described with an analytical expression that depends on only one spatial dimension, parallel to the applied field, which is also the preferred direction of profile motion during reversal. Our results provide nanodisk energy barriers as a function of the transverse field, nanodisk diameter, and material characteristics, which are useful for designing stochastic bitstreams.

Anion exchange membranes (AEMs) have attracted significant interest for their applications in fuel cells and other electrochemical devices in recent years. Understanding water distributions and hydroxide transport mechanisms within AEMs is critical to improving their performance as concerns hydroxide conductivity. Recently, nanoconfined environments have been used to mimic AEM environments. Following this approach, we construct nanoconfined cylindrical pore structures using graphane nanotubes (GNs) functionalized with trimethylammonium cations as models of local AEM morphology. These structures were then used to investigate hydroxide transport using ab initio molecular dynamics (AIMD). The simulations showed that hydroxide transport is suppressed in these confined environments relative to the bulk solution although the mechanism is dominated by structural diffusion. One factor causing the suppressed hydroxide transport is the reduced proton transfer (PT) rates due to changes in hydroxide and water solvation patterns under confinement compared to bulk solution as well as strong interactions between hydroxide ions and the tethered cation groups.

Nuclear magnetic resonance relaxometry represents a powerful tool for extracting dynamic information. Yet, obtaining links to molecular motion is challenging for many ions that relax through the quadrupolar mechanism, which is mediated by electric field gradient fluctuations and lacks a detailed microscopic description. For sodium ions in aqueous electrolytes, we combine ab initio calculations to account for electron cloud effects with classical molecular dynamics to sample long-time fluctuations, and obtain relaxation rates in good agreement with experiments over broad concentration and temperature ranges. We demonstrate that quadrupolar nuclear relaxation is sensitive to subpicosecond dynamics not captured by previous models based on water reorientation or cluster rotation. While ions affect the overall water retardation, experimental trends are mainly explained by dynamics in the first two solvation shells of sodium, which contain mostly water. This work thus paves the way to the quantitative understanding of quadrupolar relaxation in electrolyte and bioelectrolyte systems.

Phosphates and polyphosphates play ubiquitous roles in biology as integral structural components of cell membranes and bone, or as vehicles of energy storage via adenosine triphosphate and phosphocreatine. The solution phase space of phosphate species appears more complex than previously known. We present nuclear magnetic resonance (NMR) and cryogenic transmission electron microscopy (cryo-TEM) experiments that suggest phosphate species including orthophosphates, pyrophosphates, and adenosine phosphates associate into dynamic assemblies in dilute solutions that are spectroscopically "dark." Cryo-TEM provides visual evidence of the formation of spherical assemblies tens of nanometers in size, while NMR indicates that a majority population of phosphates remain as unassociated ions in exchange with spectroscopically invisible assemblies. The formation of these assemblies is reversibly and entropically driven by the partial dehydration of phosphate groups, as verified by diffusion-ordered spectroscopy (DOSY), indicating a thermodynamic state of assembly held together by multivalent interactions between the phosphates. Molecular dynamics simulations further corroborate that orthophosphates readily cluster in aqueous solutions. This study presents the surprising discovery that phosphate-containing molecules, ubiquitously present in the biological milieu, can readily form dynamic assemblies under a wide range of commonly used solution conditions, highlighting a hitherto unreported property of phosphate's native state in biological solutions.

Upon adsorption of a molecule onto a surface, the molecular energy levels (MELs) broaden and change their alignment. This phenomenon directly affects electron transfer across the interface and is, therefore, a fundamental observable that influences electrochemical device performance. Here, we propose a rigorous parameter-free framework, built upon the theoretical construct of Green's functions, for studying the interface between a molecule and a bulk surface and its effect on MELs. The method extends beyond the usual wide-band limit approximation, and its generality allows its use with any level of electronic structure theory. We demonstrate its ability to predict the broadening and shifting of MELs as a function of intramolecular coupling, molecule/surface coupling, and the surface density of states for a molecule with two MELs adsorbed on a one-dimensional model metal surface. The new approach could help provide guidelines for the design and experimental characterization of electrochemical devices with optimal electron transport.

The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event-driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co-design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware-based probabilistic computing technologies.

The Hohenberg-Kohn theorem of density-functional theory establishes the existence of a bijection between the ground-state electron density and the external potential of a many-body system. This guarantees a one-to-one map from the electron density to all observables of interest including electronic excited-state energies. Time-Dependent Density-Functional Theory (TDDFT) provides one framework to resolve this map; however, the approximations inherent in practical TDDFT calculations, together with their computational expense, motivate finding a cheaper, more direct map for electronic excitations. Here, we show that determining density and energy functionals via machine learning allows the equations of TDDFT to be bypassed. The framework we introduce is used to perform the first excited-state molecular dynamics simulations with a machine-learned functional on malonaldehyde and correctly capture the kinetics of its excited-state intramolecular proton transfer, allowing insight into how mechanical constraints can be used to control the proton transfer reaction in this molecule. This development opens the door to using machine-learned functionals for highly efficient excited-state dynamics simulations.

When the two layers of a twisted moiré system are subject to different degrees of strain, the effect is amplified by the inverse twist angle, e.g., by a factor of 50 in magic angle twisted bilayer graphene (TBG). Samples of TBG typically have heterostrains of 0.1-0.7%, increasing the bandwidth of the "flat'' bands by as much as tenfold, placing TBG in an intermediate coupling regime. Here we study the phase diagram of TBG in the presence of heterostrain with unbiased, large-scale density matrix renormalization group calculations (bond dimension $χ=24576$), including all spin and valley degrees of freedom. Working at filling $ν= -3$, we find a strain of 0.05% drives a transition from a quantized anomalous Hall insulator into an incommensurate-Kekulé spiral (IKS) phase. This peculiar order, proposed and studied at mean-field level by Kwan et al (PRX 11, 041063), breaks both valley conservation and translation symmetry T, but preserves a modified translation symmetry T' with moiré-incommensurate phase modulation. Even higher strains drive the system to a fully symmetric metal.

The anomalous Hall effect, a hallmark of broken time-reversal symmetry and spin–orbit coupling, is frequently observed in magnetically polarized systems. However, its realization in nominally non-magnetic systems remains elusive. Here, we report on the observation of the anomalous Hall effect in nominally non-magnetic KTaO3. Anomalous Hall effect emerges in reduced KTaO3 and shows an extrinsic to intrinsic crossover. A paramagnetic behavior is observed in reduced samples and confirmed using first principles calculations. The observed anomalous Hall effect follows the oxygen vacancy-induced magnetization response, suggesting that the localized magnetic moments of the oxygen vacancies scatter conduction electrons asymmetrically and give rise to the anomalous Hall effect. The anomalous Hall conductivity is found to be insensitive to the scattering rate in the low temperature limit (T < 5 K), implying that the Berry curvature of the electrons on the Fermi surface controls the anomalous Hall effect. Our observations provide a detailed picture of many-body interactions, which trigger the anomalous Hall effect in a non-magnetic system.

The Hohenberg-Kohn theorem of density functional theory establishes the existence of a bijection between the ground-state electron density and the external potential of a many-body system. This guarantees a one-to-one map from the electron density to all observables of interest including electronic excited-state energies. Time-Dependent Density-Functional Theory (TDDFT) provides one framework to resolve this map; however, the approximations inherent in practical TDDFT calculations, together with their computational expense, motivate finding a cheaper, more direct map for electronic excitations. Here, we show that determining density and energy functionals via machine learning allows the equations of TDDFT to be bypassed. The framework we introduce is used to perform the first excited-state molecular dynamics simulations with a machine-learned functional on malonaldehyde and correctly capture the kinetics of its excited-state intramolecular proton transfer, allowing insight into how mechanical constraints can be used to control the proton transfer reaction in this molecule. This development opens the door to using machine-learned functionals for highly-efficient excited-state dynamics simulations.

Many molecular crystals (approximately one third) grow as twisted, helicoidal ribbons from the melt, and this preponderance is even higher in restricted classes of materials, for instance, charge-transfer complexes. Previously, twisted crystallites of such complexes present an increase in carrier mobilities. Here, the effect of twisting on charge mobility is better analyzed for a monocomponent organic semiconductor, 2,5-bis(3-dodecyl-2-thienyl)-thiazolo[5,4-d]thiazole (BDT), that forms twisted crystals with varied helicoidal pitches and makes possible a correlation of twist strength with carrier mobility. Films are analyzed by X-ray scattering and Mueller matrix polarimetry to characterize the microscale organization of the polycrystalline ensembles. Carrier mobilities of organic field-effect transistors are five times higher when the crystals are grown with the smallest pitches (most twisted), compared to those with the largest pitches, along the fiber elongation direction. A tenfold increase is observed along the perpendicular direction. Simulation of electrical potential based on scanning electron microscopy images and density functional theory suggests that the twisting-enhanced mobility is mainly controlled by the fiber organization in the film. A greater number of tightly packed twisted fibers separated by numerous smaller gaps permit better charge transport over the film surface compared to fewer big crystallites separated by larger gaps.

Quantum time correlation functions (TCFs) involving two states are important for describing nonadiabatic dynamical processes such as charge transfer (CT). Based on a previous single-state method, we propose an imaginary-time open-chain path-integral (OCPI) approach for evaluating the two-state symmetrized TCFs. Expressing the forward and backward propagation on different electronic potential energy surfaces as a complex-time path integral, we then transform the path variables to average and difference variables such that the integration over the difference variables up to the second order can be performed analytically. The resulting expression for the symmetrized TCF is equivalent to sampling the open-chain configurations in an effective potential that corresponds to the average surface. Using importance sampling over the extended OCPI space via open path-integral molecular dynamics, we tested the resulting path-integral approximation by calculating the Fermi's golden rule CT rate constant within a widely used spin-boson model. Comparing with the real-time linearized semiclassical method and analytical result, we show that the imaginary-time OCPI provides an accurate two-state symmetrized TCF and rate constant in the typical turnover region. It is shown that the first bead of the open chain corresponds to physical zero-time and that the endpoint bead corresponds to final time t; oscillations of the end-to-end distance perfectly match the nuclear mode frequency. The two-state OCPI scheme is seen to capture the tested model's electronic quantum coherence and nuclear quantum effects accurately.

The use of electric fields to modify chemical reactions is a promising, emerging technique in catalysis. However, there exist few guiding principles, and rational design requires assumptions about the transition state or explicit atomistic calculations. Here, we present a linear free energy relationship, familiar in other areas of physical organic chemistry and catalysis, that microscopically relates field-induced changes in the activation energy to those in the reaction energy, connecting kinetic and thermodynamic behaviors. We verify our theory using first-principles electronic structure calculations of a symmetric SN2 reaction and the dehalogenation of an aryl halide on gold surfaces and observe hallmarks of linear free energy relationships, such as the shifting to early and late transition states. We also report and explain a counterintuitive case, where the constant of proportionality relating the activation and reaction energies is negative.

The use of electric fields to modify chemical reactions is a promising, emerging technique in catalysis. However, there exist few guiding principles, and rational design requires assumptions about the transition state or explicit atomistic calculations. Here, we present a linear free energy relationship, familiar in other areas of physical organic chemistry and catalysis, that microscopically relates field-induced changes in the activation energy to those in the reaction energy, connecting kinetic and thermodynamic behaviors. We verify our theory using first-principles electronic structure calculations of a symmetric S N 2 reaction and the dehalogenation of an aryl halide on gold surfaces and observe hallmarks of linear free energy relationships, such as the shifting to early and late transition states. We also report and explain a counterintuitive case, where the constant of proportionality relating the activation and reaction energies is negative, such that stabilizing the product increases the activation energy, that is, opposite of the Bell–Evans–Polanyi principle.

Nuclear magnetic resonance relaxometry represents a powerful tool for extracting dynamic information. Yet, obtaining links to molecular motion is challenging for many ions that relax through the quadrupolar mechanism, which is mediated by electric field gradient fluctuations and lacks a detailed microscopic description. For sodium ions in aqueous electrolytes, we combine ab initio calculations to account for electron cloud effects with classical molecular dynamics to sample long-time fluctuations, and obtain relaxation rates in good agreement with experiments over broad concentration and temperature ranges. We demonstrate that quadrupolar nuclear relaxation is sensitive to subpicosecond dynamics not captured by previous models based on water reorientation or cluster rotation. While ions affect the overall water retardation, experimental trends are mainly explained by dynamics in the first two solvation shells of sodium, which contain mostly water. This work thus paves the way to the quantitative understanding of quadrupolar relaxation in electrolyte and bioelectrolyte systems.

We investigated how to construct low-order subspace basis sets to accurately represent electromagnetic fields generated within inhomogeneous arbitrary objects by radio-frequency sources external to a Huygen's surface. The basis generation relies on the singular value decomposition of Green's functions integro-differential operators which makes it feasible to derive a reduced-order yet stable model. We present a detailed study of the theoretical and numerical requisites for generating such basis, and show how it can be used to calculate performance limits in magnetic resonance imaging applications. Finally, we propose a novel numerical framework for the computation of characteristic modes of arbitrary inhomogeneous objects. We validated accuracy and convergence properties of the numerical basis against a complete analytical basis in the case of a uniform spherical object. We showed that the discretization of the Huygens's surface has a minimal effect on the accuracy of the calculations, which mainly depended on the electromagnetic solver resolution and order of approximation.

Ligands that enable the delocalization of excitons beyond the physical boundary of the inorganic core of semiconductor quantum dots (QDs), called "exciton-delocalizing ligands (EDLs)", offer the opportunity to design QD-based environmental sensors with dynamically responsive optical spectra, because the degree of exciton delocalization depends on the electronic structure of the EDL. This paper demonstrates dynamic, reversible tuning of the optical bandgap of a dispersion of CdSe QDs through the redox states of their 1,3-dimesitylnaphthoquinimidazolylidene N-heterocyclic carbene (nqNHC) ligands. Upon binding of the nqNHC ligands to the QD, the optical bandgap bathochromically shifts by up to 102 meV. Electrochemical reduction of the QD-bound nqNHC ligands shifts the bandgap further by up to 25 meV, a shift that is reversible upon reoxidation.

Fuel-cell-based proton exchange membranes (PEMs) show great potential as cost-effective and clean energy conversion devices. In our recent work, we found that for the low-hydrated model PEMs with a inhomogeneous water distribution and a sulfonate anionic functional end group (SO 3 –), the H 3 O + reacts with SO 3 – according to SO 3 – + H 3 O + ↔ SO 3 H + H 2 O, indicating that the anions in PEMs become active participants in the hydronium diffusion. In this work, we use fully atomistic ab initio molecular dynamics simulations to elucidate the optimal conditions that would promote the participation of SO 3 – in the hydronium diffusion mechanism by increasing the H 3 O + /SO 3 – reactivity, thus increasing the hydronium diffusivity along the cell. The results presented in this work allow us to suggest a set of design rules for creating novel, highly conductive PEMs operating at high temperatures under a nonuniform water distribution using a linker/anion with a relatively high p K a such as (CH 2) 2 SO 3. We expect that the discovery of these key design principles will play an important role in the synthesis of high-performing materials for emerging PEM-based fuel cell technologies.

Recent studies suggest that operating anion exchange membrane (AEM) fuel cells at high temperatures has enormous technological potential. However, obtaining a fundamental understanding of the effect of temperature on hydroxide conductivity and membrane stability remains a key hurdle to realizing the full potential of high-temperature AEM fuel cells. In this work, we present a combined theoretical and experimental study to explore the effect of temperature on hydroxide ion and water diffusivities in AEMs. Both fully atomistic ab initio molecular dynamics simulations and 1 H pulsed field gradient NMR measurements confirm that the OH – diffusion changes non-monotonically with increasing temperature. Specifically, the D OH – versus T curve exhibits a region in which d D OH – /d T < 0, indicating the presence of a kink in the curve, which we refer to as a “diffusion kink”. The simulations show that the underlying causes of this behavior vary with the hydration level. Furthermore, we were able to rationalize the conditions underlying this counterintuitive behavior and to suggest ways to identify the optimal operating temperature for each model AEM system. We expect that the discovery of this unusual temperature dependence of the diffusivity will play an important role in the design of new, stable, and highly conductive AEM-based devices such as electrolyzers, redox flow batteries, and fuel cells.

Many crystals grow as banded spherulites from the melt with an optical rhythm indicative of helicoidal twisting. In this work, 23 of 41 charge transfer complexes (CTCs) are grown with twisted morphologies. As a group, CTCs more commonly twist (56%) than molecular crystals arbitrarily chosen in our previous research (31%). To analyze the effect of twisting on charge transport, three tetracyanoethylene-based CTCs with phenanthrene (PhT), pyrene (PyT), and perylene are characterized. PhT and PyT are subject to mobility measurements using organic field-effect transistors. The mobilities for twisted crystals are around three times higher than for crystals with no ostensible optical modulation, which are effectively straight. The differences in mobilities of straight and twisted crystals are considered computationally based on density functional theory. Straight crystal models built from crystallographic information files are calculated and present anisotropic hole and electron transport. For twisted crystal models, adjacent layers in the supercell are rotated by 0.01° around experimentally determined twisting directions. The modified transfer integrals lead to a slight increase (up to 25%) in the calculated mobilities of twisted crystals. Comparisons of model calculations on individual fibrils and measurements of ensembles of fibrils indicate that interfaces between single crystals are likely consequential.

Predictions of crystal structures from first-principles electronic structure calculations and molecular simulations have been performed for an energetic molecule, 4-amino-2,3,6-trinitrophenol. This physics-based approach consists of a series of steps. First, a tailor-made two-body potential energy surface (PES) was constructed with recently developed software, autoPES, using symmetry-adapted perturbation theory based on a density-functional theory description of monomers [SAPT(DFT)]. The fitting procedure ensures asymptotic correctness of the PES by employing a rigorous asymptotic multipole expansion, which seamlessly integrates with SAPT(DFT) interaction energies. Next, crystal structure prediction (CSP) was performed by generating possible crystal structures with rigid molecules, minimizing these structures using the SAPT(DFT) force field, and running isothermal–isobaric molecular dynamics (MD) simulations with flexible molecules based on the tailor-made SAPT(DFT) intermolecular force field and a generic/SAPT(DFT) intramolecular one. This workflow led to the experimentally observed structure being identified as one of the forms with the lowest lattice energy, demonstrating the success of a first-principles, bottom-up approach to CSP. Importantly, we argue that the accuracy of the intermolecular potential, here the SAPT(DFT)-based potential, is determinative of the crystal structure, while generic/SAPT(DFT) force fields can be used to represent the intramolecular potential. This force field approach simplifies the CSP workflow, without significantly compromising the accuracy of the prediction.

Simulating photon dynamics in strong light-matter coupling situations via classical trajectories is proving to be powerful and practical. Here, we analyze the performance of the approach through the lens of the exact factorization approach. Since the exact factorization enables a rigorous definition of the potentials driving the photonic motion, it allows us to identify that the underestimation of photon number and intensities observed in earlier work is primarily due to an inadequate accounting of light-matter correlation in the classical Ehrenfest force rather than errors from treating the photons quasiclassically per se. The latter becomes problematic when the number of photons per mode begins to exceed a half.

entities around the bridging P-O bonds, and is treated by a combination of MD simulations and quantum chemistry calculations. Spin-lattice relaxation was predicted well without adjustable parameters, and for SO relaxation one parameter was extracted from the comparison between experiment and computation (a correlation coefficient between the rotational motion of the groups).

using mass spectrometry and fluorescence spectroscopy. We find that this Ullmann coupling reaction occurs only on rough gold surfaces in polar solvents, the latter of which implicates interfacial electric fields. These experimental observations are supported by density functional theory calculations that elucidate the roles of surface roughness and local electric fields on the reaction. More broadly, this touchstone study offers a facile method to access and probe in real time an increasingly prominent yet incompletely understood mode of catalysis.

Imidazole and 1,2,3-triazole are promising hydrogen-bonded heterocycles that conduct protons via a structural mechanism and whose derivatives are present in systems ranging from biological proton channels to proton exchange membrane fuel cells. Here, we leverage multiple time-stepping to perform ab initio molecular dynamics of imidazole and 1,2,3-triazole at the nanosecond timescale. We show that despite the close structural similarities of these compounds, their proton diffusion constants vary by over an order of magnitude. Our simulations reveal the reasons for these differences in diffusion constants, which range from the degree of hydrogen-bonded chain linearity to the effect of the central nitrogen atom in 1,2,3-triazole on proton transport. In particular, we uncover evidence of two "blocking" mechanisms in 1,2,3-triazole, where covalent and hydrogen bonds formed by the central nitrogen atom limit the mobility of protons. Our simulations thus provide insights into the origins of the experimentally observed 10-fold difference in proton conductivity.

The anomalous Hall effect, a hallmark of broken time-reversal symmetry and spin-orbit coupling, is frequently observed in magnetically polarized systems. Its realization in non-magnetic systems, however, remains elusive. Here, we report on the observation of anomalous Hall effect in nominally non-magnetic KTaO3. Anomalous Hall effect emerges in reduced KTaO3 and shows an extrinsic to intrinsic crossover. A paramagnetic behavior is observed in reduced samples using first principles calculations and quantitative magnetometry. The observed anomalous Hall effect follows the oxygen vacancy-induced magnetization response, suggesting that the localized magnetic moments of the oxygen vacancies scatter conduction electrons asymmetrically and give rise to anomalous Hall effect. The anomalous Hall conductivity becomes insensitive to scattering rate in the low temperature limit (T<5 K), implying that the Berry curvature of the electrons on the Fermi surface controls the anomalous Hall effect. Our observations describe a detailed picture of many-body interactions, triggering anomalous Hall effect in a non-magnetic system.

Imidazole and 1,2,3-triazole are promising hydrogen-bonded heterocycles that conduct protons via a structural mechanism and whose derivatives are present in systems ranging from biological proton channels to proton exchange membrane fuel cells. Here, we leverage multiple time-stepping to perform ab initio molecular dynamics of imidazole and 1,2,3-triazole at the nanosecond time scale. We show that despite the close structural similarities of these compounds, their proton diffusion constants vary by over an order of magnitude. Our simulations reveal the reasons for these differences in diffusion constants, which range from the degree of hydrogen-bonded chain linearity to the effect of the central nitrogen atom in 1,2,3-triazole on proton transport. In particular, we uncover evidence of two “blocking” mechanisms in 1,2,3-triazole, where covalent and hydrogen bonds formed by the central nitrogen atom limit the mobility of protons. Our simulations thus provide insights into the origins of the experimentally observed 10-fold difference in proton conductivity.

Coordinatively unsaturated metal sites within certain zeolites and metal–organic frameworks can strongly adsorb a wide array of substrates. While many classical examples involve electron-poor metal cations that interact with adsorbates largely through physical interactions, unsaturated electron-rich metal centers housed within porous frameworks can often chemisorb guests amenable to redox activity or covalent bond formation. Despite the promise that materials bearing such sites hold in addressing myriad challenges in gas separations and storage, very few studies have directly interrogated mechanisms of chemisorption at open metal sites within porous frameworks. Here, we show that nondissociative chemisorption of H 2 at the trigonal pyramidal Cu + sites in the metal–organic framework Cu I-MFU-4 l occurs via the intermediacy of a metastable physisorbed precursor species. In situ powder neutron diffraction experiments enable crystallographic characterization of this intermediate, the first time that this has been accomplished for any material. Evidence for a precursor intermediate is also afforded from temperature-programmed desorption and density functional theory calculations. The activation barrier separating the precursor species from the chemisorbed state is shown to correlate with a change in the Cu + coordination environment that enhances π-backbonding with H 2. Ultimately, these findings demonstrate that adsorption at framework metal sites does not always follow a concerted pathway and underscore the importance of probing kinetics in the design of next-generation adsorbents.

Theoretical crystal structure prediction (CSP) calculations of molecular crystals often generate structures representative of the components associated with disorder, stacking faults, and polytypism. The powder X-ray diffraction (PXRD) pattern of benzamide II contains diffuse scattering consistent with a disordered structure. Previous attempts to elucidate its ordered structure by PXRD and CSP were incomplete because the average structure is actually a complex superposition of multiple polytypes, thus making structure determination particularly challenging. To address this problem, an approach to modeling such complex structures from PXRD data and CSP in combination with molecular dynamics simulations is proposed. Using this approach, the potential building blocks for polytypes possessing a two-dimensional stacking fault structure are identified. For benzamide II polytypes, centric H-bonded amide dimers are found to be the preferred H-bonding motif. The stacking faults are also associated with conformational changes of phenyl rings, in agreement with observed diffraction intensities. A characteristic average structure containing the pertinent one-dimensional motifs is derived.

Abstract Ferrimagnetic thin films are attractive for low‐power spintronic applications because of their low magnetization, small angular momentum, and fast spin dynamics. Spin orbit torques (SOT) can be applied with proximal heavy metals that also generate interfacial Dzyaloshinskii–Moriya interactions (DMI), which can stabilize ultrasmall skyrmions and enable fast domain wall motion. Here, the properties of a ferrimagnetic CoGd alloy between two heavy metals to increase the SOT efficiency, while maintaining a significant DMI is studied. SOT switching for various capping layers and alloy compositions shows that Pt/CoGd/(W or Ta) films enable more energy‐efficient SOT magnetization switching than Pt/CoGd/Ir. Spin‐torque ferromagnetic resonance confirms that Pt/CoGd/W has the highest spin‐Hall angle of 16.5%, hence SOT efficiency, larger than Pt/CoGd/(Ta or Ir). Density functional theory calculations indicate that CoGd films capped by W or Ta have the largest DMI energy, 0.38 and 0.32 mJ m−2, respectively. These results show that Pt/CoGd/W is a very promising ferrimagnetic structure to achieve small skyrmions and to move them efficiently with current.

We present a systematic analysis of our ability to tune chiral Dzyaloshinskii-Moriya interaction (DMI) in compensated ferrimagnetic Pt/GdCo/Pt1−xWx trilayers by cap layer composition. Using first-principles calculations, we show that the DMI increases rapidly for only ∼10% W and saturates thereafter, in agreement with experiments. The calculated DMI shows a spread in values around the experimental mean, depending on the atomic configuration of the cap layer interface. The saturation is attributed to the vanishing of spin-orbit coupling energy at the cap layer and the simultaneous constancy at the bottom interface. Additionally, we predict the DMI in Pt/GdCo/X (X=Ta,W,Ir) and find that W in the cap layer favors a higher DMI than Ta and Ir that can be attributed to the difference in $d$-band overlap around the Fermi level. Our results open up exciting combinatorial possibilities for controlling the DMI in ferrimagnets towards nucleating and manipulating ultrasmall high-speed skyrmions.

Fuel cell-based anion-exchange membranes (AEMs) and proton exchange membranes (PEMs) are considered to have great potential as cost-effective, clean energy conversion devices. However, a fundamental atomistic understanding of the hydroxide and hydronium diffusion mechanisms in the AEM and PEM environment is an ongoing challenge. In this work, we aim to identify the fundamental atomistic steps governing hydroxide and hydronium transport phenomena. The motivation of this work lies in the fact that elucidating the key design differences between the hydroxide and hydronium diffusion mechanisms will play an important role in the discovery and determination of key design principles for the synthesis of new membrane materials with high ion conductivity for use in emerging fuel cell technologies. To this end, ab initio molecular dynamics simulations are presented to explore hydroxide and hydronium ion solvation complexes and diffusion mechanisms in the model AEM and PEM systems at low hydration in confined environments. We find that hydroxide diffusion in AEMs is mostly vehicular, while hydronium diffusion in model PEMs is structural. Furthermore, we find that the region between each pair of cations in AEMs creates a bottleneck for hydroxide diffusion, leading to a suppression of diffusivity, while the anions in PEMs become active participants in the hydronium diffusion, suggesting that the presence of the anions in model PEMs could potentially promote hydronium diffusion.

Extended phase-space isokinetic methods in their deterministic [Minary et al., Phys. Rev. Lett. 93, 150201 (2004)] and stochastic forms [Leimkuhler et al., Mol. Phys. 111, 3579 (2013)] have proved tremendously successful in allowing multiple time-scale molecular dynamics simulations to be performed with very large time steps. These methods work by coupling the physical degrees of freedom to a set of Nosé-Hoover chain or Nosé-Hoover Langevin thermostats via an isokinetic constraint, which has the effect of avoiding resonance artifacts that plague multiple time-step algorithms. In this paper, we introduce a new resonance-free approach that achieves the same gains in time step but without the imposition of isokinetic constraints or the introduction of extended phase-space variables. Rather, we modify the physical Hamiltonian that effects the same regulation of resonances achieved by the isokinetic constraints. In so doing, we show that sampling errors can be controlled and performance improvements are possible within a simpler Hamiltonian framework. The method is demonstrated in simulations of the structure of liquid water and, in conjunction with enhanced sampling, in generation of the Ramachandran free-energy surface of the solvated alanine dipeptide.

Abstract Processing of concentrated lignocellulosic biomass suspensions typically involves the conversion of the cellulose into sugars and sugars into ethanol. Biomass is usually pretreated via methods like comminution or steam explosion to form fine cellulosic fibers to be dispersed into an aqueous phase for further treatment. The resulting cellulose suspensions need to be pressurized and pumped into and out of various processing vessels without allowing the development of flow instabilities that are typically associated with “demixing”, that is, the segregation of the cellulosic biomass from the aqueous phase via the formation of mats of cellulosic fibers and the filtration of the aqueous phase. Such demixing can prevent continuous processing at high rates. Here, the development of flow instabilities via the demixing effect for cellulose suspensions is demonstrated using capillary and compressive squeeze flows. It is shown that the use of a gelation agent, hydroxypropyl guar gum, at the critical concentration of 0.5 wt% or higher significantly affects the viscoelastic material functions of cellulosic suspensions, improves the dispersive mixing of the fibers within the aqueous phase, and results in the elimination of the flow instabilities and associated demixing effects that are ubiquitously observed during the pressurization and processing of cellulosic suspensions.

Reversible temperature-mediated solid phase changes, otherwise known as enantiotropic transformations, occur in many molecular crystals. These transformations take place as a result of the free-energy stabilization through entropic contributions at finite temperatures and can often have significant implications for the properties of crystalline solids. As such, understanding and predicting these transformations is of great importance. In this study, we utilize molecular simulations to elucidate the mechanism behind the enantiotropic layer-slip phase transformation between the orthorhombic and monoclinic versions of paracetamol form III (form III-m and III-o). Using standard molecular dynamics (MD) in addition to crystal adiabatic free-energy dynamics, an MD-based enhanced sampling approach for crystalline systems, we demonstrate that the transformation from the monoclinic form III-m to the orthorhombic form III-o is driven by localized and dynamic disorder within the structure rather than a perfect crystal-to-crystal transition. These results suggest that the orthorhombic form III-o structure does not exist as a perfect orthorhombic crystal with fully aligned layers, but rather, as an entropy-stabilized collective ensemble average of various misaligned layer-slipped structures. Overall, these simulation approaches, which explicitly treat dynamic structural disorder, allowed us to map out the free-energy landscape of this enantiotropic transformation as a function of temperature and extract critical insights into the underlying mechanism of the transformation.

This paper describes reversible "on-off" switching of the photoluminescence (PL) intensity of CdSe quantum dots (QDs), mediated by photochromic furylfulgide carboxylate (FFC) molecules chemisorbed to the surfaces of the QDs. Repeated cycles of UV and visible illumination switch the FFC between "closed" and "open" isomers. Reversible switching of the QDs' PL intensity by >80% is enabled by different rates and yields of PL-quenching photoinduced electron transfer (PET) from the QDs to the respective isomers. This difference is consistent with cyclic voltammetry measurements and density functional calculations of the isomers' frontier orbital energies. This work demonstrates fatigue-resistant modulation of the PL of a QD-molecule complex through remote control of PET. Such control potentially enables applications, such as all-optical memory, sensing, and imaging, that benefit from a fast, tunable, and reversible response to light stimuli.

Machine learning methods have become increasingly central in the development of a large variety of versatile tools for molecular simulations, many of which have the potential to advance significantly the fields of computational chemistry and physics. In this chapter, we present a framework for combining machine learning for local structure classification with the definition of a global classifier space as a basis for enhanced sampling of structural transformations in condensed phase systems. The transformation is represented by a path in classifier space, and the associated path collective variable is used to drive the process derived from changes in local structural motifs. Enhanced sampling along this type of path collective variable yields insight into the physical mechanism as well as corresponding free energy barriers of the transition. The idea is generally applicable, and the approach, as outlined here, can be adapted to a wide range of systems.

Sequence of steps in the reaction of hydronium (H 3 O +) with sulfonate (SO 3 −) contributing to the proton structural diffusion mechanism in a low-hydration environment of a proton-exchange-membrane (PEM) mimic.

Abstract The high cost of ab initio molecular dynamics (AIMD) simulations to model complex physical and chemical systems limits its ability to address many key questions. However, new machine learning‐based representations of complex potential energy surfaces have been introduced in recent years to circumvent computationally demanding AIMD simulations while retaining the same level of accuracy. As these machine learning methods gain in popularity over the next decade, it is important to address the appropriate way to develop and integrate them with well‐established simulation methods. This paper details the parameterization and training, using accurate electronic structure calculations, of artificial neural network potentials (NNPs) to model the intermolecular interactions in a hydrogen clathrate hydrate, wherein hydrogen molecules are confined inside the cavities of the 3D crystalline framework of hydrogen‐bonded water molecules. This new NNP is used in conjunction with new path‐integral based enhanced sampling methods, for inclusion of nuclear quantum effects and promotion of free energy barrier crossing, in order to determine properties that are important for understanding the diffusion of hydrogen gas in the clathrate system. These simulations demonstrate the influence of cage occupancy on the free energy barriers that determine the diffusivity of hydrogen gas through the network of large cages.

Entanglement entropy is an essential metric for characterizing quantum many-body systems, but its numerical evaluation for neural network representations of quantum states has so far been inefficient and demonstrated only for the restricted Boltzmann machine architecture. Here we estimate generalized R\'enyi entropies of autoregressive neural quantum states with up to $N=256$ spins using quantum Monte Carlo methods. A naive ``direct sampling'' approach performs well for low-order R\'enyi entropies but fails for larger orders when benchmarked on a one-dimensional Heisenberg model. We therefore propose an improved ``conditional sampling'' method exploiting the autoregressive structure of the network ansatz, which outperforms direct sampling and facilitates calculations of higher-order R\'enyi entropies in both one- and two-dimensional Heisenberg models. Access to higher-order R\'enyi entropies allows for an approximation of the von Neumann entropy as well as extraction of the single-copy entanglement. Both methods elucidate the potential of neural network quantum states in quantum Monte Carlo studies of entanglement entropy for many-body systems.

Deep eutectic solvents (DESs) are an emerging class of mixtures characterized by significant depressions in melting points compared to those of the neat constituent components. These materials are promising for applications as inexpensive "designer" solvents exhibiting a host of tunable physicochemical properties. A detailed review of the current literature reveals the lack of predictive understanding of the microscopic mechanisms that govern the structure-property relationships in this class of solvents. Complex hydrogen bonding is postulated as the root cause of their melting point depressions and physicochemical properties; to understand these hydrogen bonded networks, it is imperative to study these systems as dynamic entities using both simulations and experiments. This review emphasizes recent research efforts in order to elucidate the next steps needed to develop a fundamental framework needed for a deeper understanding of DESs. It covers recent developments in DES research, frames outstanding scientific questions, and identifies promising research thrusts aligned with the advancement of the field toward predictive models and fundamental understanding of these solvents.

In multiple time scale molecular dynamics, the use of isokinetic constraints along with massive thermostatting has enabled the adoption of very large integration steps, well beyond the limits imposed by resonance artifacts in standard algorithms. In this work, we present two new contributions to this topic. First, we investigate the velocity distribution and the temperature-kinetic energy relationship associated with the isokinetic Nosé-Hoover family of methods, showing how they depend on the number of thermostats attached to each atomic degree of freedom. Second, we investigate the performance of these methods in the calculation of solvation free energies, the determination of which is often key for understanding the partition of a chemical species among distinct environments. We show how one can extract this property from canonical (constant-NVT) simulations and compare the result to experimental data obtained at a specific pressure. Finally, we demonstrate that large time steps can, in fact, be used to improve the efficiency of these calculations and that attaching multiple thermostats per degree of freedom is beneficial for effectively exploring the configurational space of a molecular system.

We study the aqueous solvation dynamics of lithium ions using nuclear magnetic resonance spectroscopy, molecular dynamics, and viscosity measurements. Several relaxation mechanisms are examined to explain the strong increases of spin-lattice relaxation toward high concentrations. The use of both 6Li and 7Li isotopes is helpful to identify the quadrupolar contribution to the relaxation rate. In particular, it is found that the quadrupolar interaction constitutes the strongest contribution above a concentration of ∼10 molal. The next-strongest contribution arises from interactions that scale with the square of the gyromagnetic ratio (mostly the dipolar interaction), and the experimental relaxation rates appear to be fully accounted for when these mechanisms are combined over the concentration range up to the saturation concentration. The study of solvation dynamics, particularly at high concentrations, could be of relevance for electrolyte dynamics in aqueous Li-ion rechargeable batteries.

Predicting structures of organic molecular cocrystals is a challenging task when considering the immense number of possible intermolecular orientations. Use of the Shannon information entropy, constructed from an intermolecular orientational spatial distribution function, to drive a search for crystal structures via enhanced molecular dynamics can be an efficient way to map out a landscape of putative polymorphs. Here, the Shannon entropy is used to generate a set of collective variables for differentiating polymorphs of a 1:1 cocrystal of resorcinol and urea. We show that driven adiabatic free energy dynamics, a particular enhanced-sampling approach, combined with these entropy variables, can transform the stable phase into alternate polymorphs. Density functional theory calculations confirm that a structure obtained from the enhanced molecular dynamics is stable at pressures above 1 GPa. We thus show that enhanced sampling should be considered an integral component of crystal structure searching protocols for systems with multiple independent molecules.

Abstract Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal ⋅ mol −1 with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal ⋅ mol −1) on test data. Moreover, density-based Δ -learning (learning only the correction to a standard DFT calculation, termed Δ -DFT) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Δ -DFT is highlighted by correcting “on the fly” DFT-based molecular dynamics (MD) simulations of resorcinol (C 6 H 4 (OH) 2) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Δ -DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails.

The standard description of cavity-modified molecular reactions typically involves a single (resonant) mode, while in reality, the quantum cavity supports a range of photon modes. Here, we demonstrate that as more photon modes are accounted for, physicochemical phenomena can dramatically change, as illustrated by the cavity-induced suppression of the important and ubiquitous process of proton-coupled electron-transfer. Using a multi-trajectory Ehrenfest treatment for the photon-modes, we find that self-polarization effects become essential, and we introduce the concept of self-polarization-modified Born-Oppenheimer surfaces as a new construct to analyze dynamics. As the number of cavity photon modes increases, the increasing deviation of these surfaces from the cavity-free Born-Oppenheimer surfaces, together with the interplay between photon emission and absorption inside the widening bands of these surfaces, leads to enhanced suppression. The present findings are general and will have implications for the description and control of cavity-driven physical processes of molecules, nanostructures, and solids embedded in cavities.

Living systems have the unique ability to form hierarchical assemblies, in which individual constituents can perform tasks cooperatively and emergently. Harnessing such properties is a long-standing challenge for the rational design of dynamic materials, that can respond to their environment, communicate with one another, and undergo a rapid, reversible, assembly through the transduction of energy. Recent developments in the design of smart and active colloidal building blocks have led to tremendous breakthroughs, with, for instance, the onset of synthetic photoactivated active assemblies. In this project, we develop a combined experimental, computational, theoretical and Machine Learning framework to shed light on the physical underpinnings of such assembly processes and program the assembly of smart active materials.

The hydrated morphology of anion-exchange membranes (used in alkaline membrane fuel cells) is a key aspect in determining their ion conductivity. Herein, we examine the changes in the morphology of hydrated polystyrene- b-poly(ethylene- co-butylene)- b-polystyrene functionalized with quaternary ammonium groups in the polystyrene phase. The effects of the degree of functionalization and styrene content were studied using dissipative particle dynamics simulations. The morphology was controlled by the following three factors: the hydration level, the degree of functionalization of styrene blocks, and the percentage of styrene in the block copolymers. Perfect lamellae, imperfect lamellae, disordered bicontinuous structures, gyroid-like structures, and the coexistence of layered structures and micelles were all observed under particular conditions. Among the three types, the imperfect lamella was made up of slices or subdomains of perfect lamella. These lamellae formed in different directions and were interconnected by the transitional regions. Pure water domains were formed at a high water content (λ ≡ n H 2 O/TMA + ≥ 16), and the lowest degree of functionalization (25%) may require the highest hydration. An analysis of the clustering of water molecules revealed that a full percolating aqueous domain occurs when the hydration level reaches approximately λ = 12. Lowering of the functionalization and/or the styrene percentage was observed to postpone the formation of large connected water domains, but increasing the styrene percentage enables the percolation to occur at a lower water content.