Pub Date : 2026-10-23DOI: 10.1021/acs.jctc.6c01706
Jayakrushna Sahoo,Brian K. Kendrick,Dmitri Babikov
Mixed Quantum/Classical Theory (MQCT) offers an efficient alternative for computing molecular scattering cross sections, scaling favorably with the size of the basis as compared to the exact close-coupling methods. In this work, the MQCT framework is extended to the rotationally inelastic scattering of 2Π molecules with structureless targets, using the Ar + NO (X2Π) system as a benchmark. The time-dependent equations of motion are solved for independent MQCT trajectories by treating the translational motion classically and the electronic and rotational motions quantum mechanically. This formalism naturally incorporates electronic nonadiabatic couplings, enabling calculations of state-to-state cross sections across all possible transitions, including pure rotational excitation, transitions between spin–orbit manifolds and between the individual states of Λ-doublet components. For validation purposes, these are compared against exact full-quantum calculations using HIBRIDON package. Good quantitative agreement is observed, with complicated quantum interference effects successfully reproduced, specifically capturing major features of the Λ-doublet propensity.
混合量子/经典理论（MQCT）为计算分子散射截面提供了一种有效的替代方法，与精确的紧密耦合方法相比，它与基的大小呈正比。在这项工作中，使用Ar + NO （X2Π）体系作为基准，MQCT框架扩展到具有无结构目标的2Π分子的旋转非弹性散射。通过经典地处理平移运动和量子力学地处理电子运动和旋转运动，求解了独立的MQCT运动轨迹的时间相关运动方程。这种形式自然地结合了电子非绝热耦合，可以计算所有可能跃迁的状态到状态截面，包括纯旋转激发，自旋轨道流形之间的跃迁以及Λ-doublet组件的各个状态之间的跃迁。为了验证目的，将这些与使用HIBRIDON软件包的精确全量子计算进行比较。观察到良好的定量一致，成功地再现了复杂的量子干涉效应，特别是捕获了Λ-doublet倾向的主要特征。
{"title":"Mixed Quantum/Classical Theory for Fine-Structure Inelastic Transitions in the Scattering of 2Π Molecule with Structureless Target","authors":"Jayakrushna Sahoo,Brian K. Kendrick,Dmitri Babikov","doi":"10.1021/acs.jctc.6c01706","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01706","url":null,"abstract":"Mixed Quantum/Classical Theory (MQCT) offers an efficient alternative for computing molecular scattering cross sections, scaling favorably with the size of the basis as compared to the exact close-coupling methods. In this work, the MQCT framework is extended to the rotationally inelastic scattering of 2Π molecules with structureless targets, using the Ar + NO (X2Π) system as a benchmark. The time-dependent equations of motion are solved for independent MQCT trajectories by treating the translational motion classically and the electronic and rotational motions quantum mechanically. This formalism naturally incorporates electronic nonadiabatic couplings, enabling calculations of state-to-state cross sections across all possible transitions, including pure rotational excitation, transitions between spin–orbit manifolds and between the individual states of Λ-doublet components. For validation purposes, these are compared against exact full-quantum calculations using HIBRIDON package. Good quantitative agreement is observed, with complicated quantum interference effects successfully reproduced, specifically capturing major features of the Λ-doublet propensity.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"14 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149241650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-23DOI: 10.1021/acs.jctc.6c01326
Roel van de Ven,Herma Cuppen,Daria Ruth Galimberti
We present a new enhanced-sampling scheme for obtaining the underlying conformer starting from a spectrum: the SPectroscopy Informed Configurational Enhanced Sampling (SPICES) method. SPICES is driven by a history-dependent biased molecular dynamics, in which the biases are governed by spectral information rather than by energetic considerations. The performance of SPICES is hence rather insensitive to the target conformer’s relative energy compared to the global minimum structure. Furthermore, it does not require pretraining or prior knowledge of the system. We have critically assessed the performance of SPICES and demonstrated that it can identify the correct conformer of our test system, diphenylalanine (FF), among the 400 stable conformations in 22 spectral calculations (median value). We also show that SPICES is robust to noise in the experimental spectrum and to small discrepancies between theory and experiment.
{"title":"SPectroscopy Informed Configurational Enhanced Sampling (SPICES)","authors":"Roel van de Ven,Herma Cuppen,Daria Ruth Galimberti","doi":"10.1021/acs.jctc.6c01326","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01326","url":null,"abstract":"We present a new enhanced-sampling scheme for obtaining the underlying conformer starting from a spectrum: the SPectroscopy Informed Configurational Enhanced Sampling (SPICES) method. SPICES is driven by a history-dependent biased molecular dynamics, in which the biases are governed by spectral information rather than by energetic considerations. The performance of SPICES is hence rather insensitive to the target conformer’s relative energy compared to the global minimum structure. Furthermore, it does not require pretraining or prior knowledge of the system. We have critically assessed the performance of SPICES and demonstrated that it can identify the correct conformer of our test system, diphenylalanine (FF), among the 400 stable conformations in 22 spectral calculations (median value). We also show that SPICES is robust to noise in the experimental spectrum and to small discrepancies between theory and experiment.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"14 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149241768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-23DOI: 10.1021/acs.jctc.6c01383
Yichi Zhang,Jun Yang
We generalize the augmented Roothaan–Hall (ARH) Hessian formalism to the self-consistent field (SCF) optimization of spin-restricted open-shell (RO) wave functions, encompassing high-spin, low-spin, and two-determinant electronic states. A detailed ARH formulation is presented. We demonstrate that ARH is a highly efficient optimization algorithm for rapidly identifying accurate SCF solutions, primarily owing to its systematic construction of an effective Hessian, particularly in the case of Euclidean quadratic energy functions. The ARH is built upon a universal energy formulation, including grid-based integration, for spin-restricted closed-shell, spin-unrestricted, and RO density functional theory (DFT), thereby unifying and simplifying their numerical implementation. The performance of the present method is evaluated using two benchmarking studies. First, for a series of iron–sulfur clusters exhibiting different spin states, which represent notoriously challenging SCF problems, the ARH algorithm demonstrates superior convergence efficiency relative to L-BFGS and truncated Newton methods, requiring much fewer RO-SCF iterations to achieve convergence. Second, the ARH approach tends to avoid convergence to higher-energy stationary points in two-determinant RO-SCF calculations for singlet excited states of selected photoactive compounds. Finally, an application of the ARH-based RO-SCF is illustrated by an investigation of the mechanistic origin of the spin-crossover phenomenon in a Ni(II)-porphyrin complex utilized as a contrast agent.
{"title":"Augmented Roothaan-Hall Hessian Applied to Spin-Restricted Open-Shell Density Functional Theory","authors":"Yichi Zhang,Jun Yang","doi":"10.1021/acs.jctc.6c01383","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01383","url":null,"abstract":"We generalize the augmented Roothaan–Hall (ARH) Hessian formalism to the self-consistent field (SCF) optimization of spin-restricted open-shell (RO) wave functions, encompassing high-spin, low-spin, and two-determinant electronic states. A detailed ARH formulation is presented. We demonstrate that ARH is a highly efficient optimization algorithm for rapidly identifying accurate SCF solutions, primarily owing to its systematic construction of an effective Hessian, particularly in the case of Euclidean quadratic energy functions. The ARH is built upon a universal energy formulation, including grid-based integration, for spin-restricted closed-shell, spin-unrestricted, and RO density functional theory (DFT), thereby unifying and simplifying their numerical implementation. The performance of the present method is evaluated using two benchmarking studies. First, for a series of iron–sulfur clusters exhibiting different spin states, which represent notoriously challenging SCF problems, the ARH algorithm demonstrates superior convergence efficiency relative to L-BFGS and truncated Newton methods, requiring much fewer RO-SCF iterations to achieve convergence. Second, the ARH approach tends to avoid convergence to higher-energy stationary points in two-determinant RO-SCF calculations for singlet excited states of selected photoactive compounds. Finally, an application of the ARH-based RO-SCF is illustrated by an investigation of the mechanistic origin of the spin-crossover phenomenon in a Ni(II)-porphyrin complex utilized as a contrast agent.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"30 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149241649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-22DOI: 10.1021/acs.jctc.6c01007
Howook Hwang,Mee Shelley,Andrew Placzek,Kevin DeMarco,Steven Albanese,Andreas Verras,Neelu Kaila,Joshua McElwee,Sebastien Campos,Justin A. Caravella,Samantha Carreiro,Scott D. Edmondson,Kevin D. Kreutter,Sheetal Kumar,Angela V. Toms,Peter J. Tummino,Lewis Whitehead,Sung-Sau So,Daigo Inoyama,Kyle Konze,Robert Pelletier,Tianchuan Xu,Robert Abel,Richard A. Friesner,Edward B. Miller
The development of a small molecule suitable for clinical use involves optimization of ligand binding to an identified target protein but also frequently the reduction of binding to a variety of anti-target proteins known to pose translational risk. These anti-targets, often referred to as ADMET proteins, often contain large, highly flexible binding sites, leading to promiscuous binding of small molecules. This property often frustrates efforts to execute structure-based design against these anti-targets. Here, we demonstrate the application of a combination of induced fit docking and free energy perturbation to structurally enable several of the most common anti-targets: cytochrome P450 isoforms 3A4 and 2D6, the hERG ion channel, and PXR. Crucially, the enforcement of a consensus binding mode across a congeneric series allowed for the efficient exploration of a wide range of receptor conformational space. Results are presented for both public retrospective datasets and prospective application in active drug discovery programs.
{"title":"Accelerating Lead Optimization Away from Anti-Targets: Navigating the Structural Complexity of Promiscuous ADMET Proteins","authors":"Howook Hwang,Mee Shelley,Andrew Placzek,Kevin DeMarco,Steven Albanese,Andreas Verras,Neelu Kaila,Joshua McElwee,Sebastien Campos,Justin A. Caravella,Samantha Carreiro,Scott D. Edmondson,Kevin D. Kreutter,Sheetal Kumar,Angela V. Toms,Peter J. Tummino,Lewis Whitehead,Sung-Sau So,Daigo Inoyama,Kyle Konze,Robert Pelletier,Tianchuan Xu,Robert Abel,Richard A. Friesner,Edward B. Miller","doi":"10.1021/acs.jctc.6c01007","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01007","url":null,"abstract":"The development of a small molecule suitable for clinical use involves optimization of ligand binding to an identified target protein but also frequently the reduction of binding to a variety of anti-target proteins known to pose translational risk. These anti-targets, often referred to as ADMET proteins, often contain large, highly flexible binding sites, leading to promiscuous binding of small molecules. This property often frustrates efforts to execute structure-based design against these anti-targets. Here, we demonstrate the application of a combination of induced fit docking and free energy perturbation to structurally enable several of the most common anti-targets: cytochrome P450 isoforms 3A4 and 2D6, the hERG ion channel, and PXR. Crucially, the enforcement of a consensus binding mode across a congeneric series allowed for the efficient exploration of a wide range of receptor conformational space. Results are presented for both public retrospective datasets and prospective application in active drug discovery programs.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"12 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149230064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-22DOI: 10.1021/acs.jctc.6c01541
Benedict Tan,Alessio Ciulli,Julien Michel
Molecular glue-induced targeted protein degradation is a powerful emerging therapeutic modality that exploits the ubiquitin-proteasome degradation pathway to break down proteins associated with disease proliferation. At the structural level, degradation proceeds through the formation of a ternary complex induced by small molecules between the target protein and an E3 ligase. Unlike PROTACs, molecular glues do not require a chemical linker and tend to stabilize preexisting native protein–protein interactions. Computational screening of novel glues is challenging, in large part due to the complexity and high degree of cooperativity involved in the formation of the ternary complex interface. Here, we present a rigorous Molecular Dynamics-based simulation protocol that uses Umbrella Sampling to calculate the ternary complex binding free energy. The protocol follows a geometric thermodynamic cycle with conformational and Boresch-style orientational restraints to drive the system from bound to unbound. Our framework successfully predicts the effect of single-point interface mutations on the relative stability of the CK1α-lenalidomide-CRBN ternary complex (R2 = 0.95 compared to experimental binding affinities). We also study a series of systems related to the BRD4 degraders IBG1 and IBG3, which recruit the DCAF16 E3 ligase. We rank various complexes engaging DCAF16 by their binding free energies and achieve an ordering that is consistent with the trends observed in biophysical assays. Of particular note is our ability to assert the reliance of BRD4 selectivity on a single interfacial glycine residue, which alternative, reduced-sampling MM/GBSA or HADDOCK scoring methodologies fail to achieve. Overall, our protocol shows promise as a generalizable approach to ranking ternary complexes by thermodynamic stability, potentially aiding in the identification of potent new degraders.
{"title":"Predicting Ternary Complex Stability through Binding Free-Energy Calculations in the Geometric Route","authors":"Benedict Tan,Alessio Ciulli,Julien Michel","doi":"10.1021/acs.jctc.6c01541","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01541","url":null,"abstract":"Molecular glue-induced targeted protein degradation is a powerful emerging therapeutic modality that exploits the ubiquitin-proteasome degradation pathway to break down proteins associated with disease proliferation. At the structural level, degradation proceeds through the formation of a ternary complex induced by small molecules between the target protein and an E3 ligase. Unlike PROTACs, molecular glues do not require a chemical linker and tend to stabilize preexisting native protein–protein interactions. Computational screening of novel glues is challenging, in large part due to the complexity and high degree of cooperativity involved in the formation of the ternary complex interface. Here, we present a rigorous Molecular Dynamics-based simulation protocol that uses Umbrella Sampling to calculate the ternary complex binding free energy. The protocol follows a geometric thermodynamic cycle with conformational and Boresch-style orientational restraints to drive the system from bound to unbound. Our framework successfully predicts the effect of single-point interface mutations on the relative stability of the CK1α-lenalidomide-CRBN ternary complex (R2 = 0.95 compared to experimental binding affinities). We also study a series of systems related to the BRD4 degraders IBG1 and IBG3, which recruit the DCAF16 E3 ligase. We rank various complexes engaging DCAF16 by their binding free energies and achieve an ordering that is consistent with the trends observed in biophysical assays. Of particular note is our ability to assert the reliance of BRD4 selectivity on a single interfacial glycine residue, which alternative, reduced-sampling MM/GBSA or HADDOCK scoring methodologies fail to achieve. Overall, our protocol shows promise as a generalizable approach to ranking ternary complexes by thermodynamic stability, potentially aiding in the identification of potent new degraders.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"76 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-22DOI: 10.1021/acs.jctc.6c01816
Junfeng Lu,Yucong Ge,Yanlei Wang,Hongyan He
Machine-learned force fields (MLFFs) based on Neuroevolution Potential (NEP) models were developed for the ionic liquid 1-ethyl-3-methylimidazolium acetate ([Emim][Ac]) using first-principles reference data generated with four density functionals: PBE-D3, RPBE-D3, optB88-vdW, and SCAN+rVV10. Using a unified training protocol, the resulting NEP models were systematically benchmarked against structural, thermophysical, transport, and hydrogen-bond-network properties, including radial distribution functions (RDFs), coordination numbers, neutron total scattering profiles, density, viscosity, ionic conductivity, and thermal conductivity. Spearman correlation analysis and weighted composite scoring were further used to establish a property-oriented evaluation framework for MLFF assessment and selection. The results reveal a pronounced dependence of NEP performance on the density functional used to generate the reference data. Although all four models reproduce the dominant local structural features of [Emim][Ac], appreciable differences emerge in medium-range structural ordering and transport properties. The PBE-D3-based NEP provides the most accurate overall description of structural properties, whereas the optB88-vdW-based NEP yields the closest agreement with experimental transport properties, particularly viscosity and ionic conductivity. Analysis of the hydrogen-bond network further reveals systematic relationships among hydrogen-bond populations, residence times, free-ion fractions, and ion transport. These results demonstrate that, for [Emim][Ac], the exchange–correlation functional used for reference-data generation influences the physical fidelity and property-specific performance of the resulting NEP models.
{"title":"Performance of Neuroevolution Potentials for the Ionic Liquid [Emim][Ac]: A Systematic Benchmark of Four Density Functionals","authors":"Junfeng Lu,Yucong Ge,Yanlei Wang,Hongyan He","doi":"10.1021/acs.jctc.6c01816","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01816","url":null,"abstract":"Machine-learned force fields (MLFFs) based on Neuroevolution Potential (NEP) models were developed for the ionic liquid 1-ethyl-3-methylimidazolium acetate ([Emim][Ac]) using first-principles reference data generated with four density functionals: PBE-D3, RPBE-D3, optB88-vdW, and SCAN+rVV10. Using a unified training protocol, the resulting NEP models were systematically benchmarked against structural, thermophysical, transport, and hydrogen-bond-network properties, including radial distribution functions (RDFs), coordination numbers, neutron total scattering profiles, density, viscosity, ionic conductivity, and thermal conductivity. Spearman correlation analysis and weighted composite scoring were further used to establish a property-oriented evaluation framework for MLFF assessment and selection. The results reveal a pronounced dependence of NEP performance on the density functional used to generate the reference data. Although all four models reproduce the dominant local structural features of [Emim][Ac], appreciable differences emerge in medium-range structural ordering and transport properties. The PBE-D3-based NEP provides the most accurate overall description of structural properties, whereas the optB88-vdW-based NEP yields the closest agreement with experimental transport properties, particularly viscosity and ionic conductivity. Analysis of the hydrogen-bond network further reveals systematic relationships among hydrogen-bond populations, residence times, free-ion fractions, and ion transport. These results demonstrate that, for [Emim][Ac], the exchange–correlation functional used for reference-data generation influences the physical fidelity and property-specific performance of the resulting NEP models.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"17 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149226499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-22DOI: 10.1021/acs.jctc.6c01260
Yu-Wei Sun,Yong-Qing Chen,Tong Li,Zhan-Wei Li
Polar ordering in liquid crystals arises from the interplay between molecular anisotropy and long-range dipole–dipole interactions, making molecular dynamics simulations of formation kinetics computationally demanding due to the cost of evaluating these interactions. Here, we develop a high-performance mesoscale molecular dynamics framework for efficient large-scale simulations of dipolar liquid-crystal systems. The framework incorporates GPU- and DCU-accelerated algorithms for efficiently computing anisotropic Gay-Berne forces and long-range dipole–dipole interactions, and supports generalized Gay-Berne particles carrying an arbitrary number of embedded dipoles with freely assignable positions and orientations. The algorithm employs a multithreaded parallelization strategy with adaptive optimization, yielding significant speedups on heterogeneous computing platforms. We validate the framework against representative ferroelectric and antiferroelectric systems, faithfully reproducing known phase behavior while maintaining computational efficiency. This work provides a versatile molecular dynamics framework for investigating polar ordering and its formation kinetics across a wide range of liquid-crystal systems and dipolar assemblies.
{"title":"A High-Performance Mesoscale Molecular Dynamics Framework for Polar Ordering in Dipolar Liquid Crystals","authors":"Yu-Wei Sun,Yong-Qing Chen,Tong Li,Zhan-Wei Li","doi":"10.1021/acs.jctc.6c01260","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01260","url":null,"abstract":"Polar ordering in liquid crystals arises from the interplay between molecular anisotropy and long-range dipole–dipole interactions, making molecular dynamics simulations of formation kinetics computationally demanding due to the cost of evaluating these interactions. Here, we develop a high-performance mesoscale molecular dynamics framework for efficient large-scale simulations of dipolar liquid-crystal systems. The framework incorporates GPU- and DCU-accelerated algorithms for efficiently computing anisotropic Gay-Berne forces and long-range dipole–dipole interactions, and supports generalized Gay-Berne particles carrying an arbitrary number of embedded dipoles with freely assignable positions and orientations. The algorithm employs a multithreaded parallelization strategy with adaptive optimization, yielding significant speedups on heterogeneous computing platforms. We validate the framework against representative ferroelectric and antiferroelectric systems, faithfully reproducing known phase behavior while maintaining computational efficiency. This work provides a versatile molecular dynamics framework for investigating polar ordering and its formation kinetics across a wide range of liquid-crystal systems and dipolar assemblies.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"63 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149230155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-22DOI: 10.1021/acs.jctc.6c01159
Magdalena Häupl,Sebastian Falkner,Peter G. Bolhuis,Christoph Dellago,Alessandro Coretti
We present a one-way shooting algorithm for transition path sampling that accepts every proposed trajectory while sampling the correct transition path ensemble for systems with overdamped stochastic dynamics. The method combines an always reactive path generation scheme with a reweighting procedure that corrects the bias introduced by unit acceptance, improving efficiency by avoiding rejected trajectories. We show its performance on CO2 hydrate formation, where the enhanced efficiency enables the sampling of crystalline channels under conditions inaccessible to conventional algorithms.
{"title":"An Always-Accepting Algorithm for Transition Path Sampling","authors":"Magdalena Häupl,Sebastian Falkner,Peter G. Bolhuis,Christoph Dellago,Alessandro Coretti","doi":"10.1021/acs.jctc.6c01159","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01159","url":null,"abstract":"We present a one-way shooting algorithm for transition path sampling that accepts every proposed trajectory while sampling the correct transition path ensemble for systems with overdamped stochastic dynamics. The method combines an always reactive path generation scheme with a reweighting procedure that corrects the bias introduced by unit acceptance, improving efficiency by avoiding rejected trajectories. We show its performance on CO2 hydrate formation, where the enhanced efficiency enables the sampling of crystalline channels under conditions inaccessible to conventional algorithms.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"12 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-10-22DOI: 10.1021/acs.jctc.6c01494
Jiankun Wu,Jinming Fan,Chao Qian,Shaodong Zhou
Linear-combination-of-atomic-orbital (LCAO) density functional theory (DFT) incurs steep costs when it constructs Coulomb terms and iterates the self-consistent field (SCF) equations. Matrix-learning approaches can bypass parts of this workflow, but their outputs inherit the dimensions and conventions of a fixed orbital basis. We introduce DeepHartree, a Poisson-coupled neural field that connects continuous real-space prediction to LCAO electronic structure. An E(3)-equivariant network predicts the Hartree potential, and the Poisson equation converts this potential into electron density. Atom-centered Gaussian fields resolve the near-nuclear region, while a molecule-level correction enforces the electron count. Analytical Gaussian integrals and numerical quadrature connect the predicted fields to the Kohn–Sham matrix. One diagonalization provides estimates of frontier orbital energies and the occupied subspace, and yields the density matrix used to evaluate energy components and initialize SCF. Mean molecular weighted normalized mean absolute errors (wNMAEs) are 0.110% for QM9 and 0.199% for VQM24 on PySCF level-0 grids, increasing to 1.366% and 2.218%, respectively, on level-3 grids. Models trained on def2-SVP densities transfer without retraining to def2-TZVP and def2-QZVP, retaining total-energy accuracy and occupied-subspace agreement. The Hybrid method combines finite differences and first-order automatic differentiation with analytical Gaussian integrals and multilevel quadrature to accelerate field-to-matrix conversion. It achieves near-linear density-matrix construction in both alkanes and PEG chains. For the 1004-atom PEG chain, this construction is 8.75-fold faster than full AD and 7.34-fold faster than converged GPU4PySCF SCF. Without fine-tuning, the QM9 model attains a total-energy MAE of 9.437 meV atom–1 on 1000 larger OE62 molecules. Across the three basis sets, DeepHartree initialization reduces mean QM9 SCF iteration counts by 15.3–15.6%. For a 620-atom organic cage, it reduces total SCF wall-clock time by 12.9%, including initialization. These results establish continuous electrostatic fields as an accurate and scalable interface between machine learning and LCAO DFT.
{"title":"DeepHartree: A Poisson-Coupled Neural Field for One-Shot Density Functional Theory","authors":"Jiankun Wu,Jinming Fan,Chao Qian,Shaodong Zhou","doi":"10.1021/acs.jctc.6c01494","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01494","url":null,"abstract":"Linear-combination-of-atomic-orbital (LCAO) density functional theory (DFT) incurs steep costs when it constructs Coulomb terms and iterates the self-consistent field (SCF) equations. Matrix-learning approaches can bypass parts of this workflow, but their outputs inherit the dimensions and conventions of a fixed orbital basis. We introduce DeepHartree, a Poisson-coupled neural field that connects continuous real-space prediction to LCAO electronic structure. An E(3)-equivariant network predicts the Hartree potential, and the Poisson equation converts this potential into electron density. Atom-centered Gaussian fields resolve the near-nuclear region, while a molecule-level correction enforces the electron count. Analytical Gaussian integrals and numerical quadrature connect the predicted fields to the Kohn–Sham matrix. One diagonalization provides estimates of frontier orbital energies and the occupied subspace, and yields the density matrix used to evaluate energy components and initialize SCF. Mean molecular weighted normalized mean absolute errors (wNMAEs) are 0.110% for QM9 and 0.199% for VQM24 on PySCF level-0 grids, increasing to 1.366% and 2.218%, respectively, on level-3 grids. Models trained on def2-SVP densities transfer without retraining to def2-TZVP and def2-QZVP, retaining total-energy accuracy and occupied-subspace agreement. The Hybrid method combines finite differences and first-order automatic differentiation with analytical Gaussian integrals and multilevel quadrature to accelerate field-to-matrix conversion. It achieves near-linear density-matrix construction in both alkanes and PEG chains. For the 1004-atom PEG chain, this construction is 8.75-fold faster than full AD and 7.34-fold faster than converged GPU4PySCF SCF. Without fine-tuning, the QM9 model attains a total-energy MAE of 9.437 meV atom–1 on 1000 larger OE62 molecules. Across the three basis sets, DeepHartree initialization reduces mean QM9 SCF iteration counts by 15.3–15.6%. For a 620-atom organic cage, it reduces total SCF wall-clock time by 12.9%, including initialization. These results establish continuous electrostatic fields as an accurate and scalable interface between machine learning and LCAO DFT.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149229970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The electronic and spectroscopic properties of aqueous hydrophobic interfaces continue to be a topic of active research. One such open question is rooted in the origin of the apparent negative charge of oil droplets. Here, we deploy an information-theoretic optimal transport approach dubbed VIPER-CT (Voronoi-Informed Partitioning of Electron Redistribution for Charge Transfer), to construct the electronic T-matrix derived from the real-space electron density determined from first-principles simulations of an extended decane-water interface, sampled using neural-network-based Deep Potential Molecular Dynamics. This matrix operationally separates directional interphase charge transfer (CT) from intraphase density rearrangements. While decane-water molecular clusters of various sizes show small net CT from decane to water, the extended interface displays a systematic opposite electronic asymmetry, yielding a net CT from water to the hydrocarbon phase producing an average surface charge density of ∼0.003 e– nm–2 on the oil phase. This imbalance is accompanied by much larger intraphase self-polarization, particularly within the hydrocarbon phase, demonstrating that collective many-body polarization dominates the interfacial electronic response.
疏水界面的电子和光谱性质一直是研究的热点。其中一个悬而未决的问题是油滴的明显负电荷的来源。在这里，我们部署了一种称为VIPER-CT（电荷转移电子重分配的voroni - informed Partitioning of Electron Redistribution for Charge Transfer）的信息论最佳传输方法，构建了电子t矩阵，该矩阵来源于扩展的十烷-水界面的第一线原理模拟确定的实空间电子密度，使用基于神经网络的深势分子动力学进行采样。该矩阵可将定向相间电荷转移（CT）与相内密度重排分离。虽然不同大小的癸烷-水分子簇从癸烷到水的净CT值较小，但扩展界面显示出系统的相反电子不对称，产生从水到烃相的净CT值，在油相上产生平均表面电荷密度为~ 0.003 e- nm-2。这种不平衡伴随着更大的相内自极化，特别是在烃相内，表明集体多体极化主导了界面电子响应。
{"title":"Mapping Collective Electron-Density Rearrangements at Oil–Water Interfaces Using Optimal Transport","authors":"Gabriele Amante,Klaudia Mráziková,Gabriele Centi,Sylvie Roke,Ali Hassanali,Giuseppe Cassone","doi":"10.1021/acs.jctc.6c01556","DOIUrl":"https://doi.org/10.1021/acs.jctc.6c01556","url":null,"abstract":"The electronic and spectroscopic properties of aqueous hydrophobic interfaces continue to be a topic of active research. One such open question is rooted in the origin of the apparent negative charge of oil droplets. Here, we deploy an information-theoretic optimal transport approach dubbed VIPER-CT (Voronoi-Informed Partitioning of Electron Redistribution for Charge Transfer), to construct the electronic T-matrix derived from the real-space electron density determined from first-principles simulations of an extended decane-water interface, sampled using neural-network-based Deep Potential Molecular Dynamics. This matrix operationally separates directional interphase charge transfer (CT) from intraphase density rearrangements. While decane-water molecular clusters of various sizes show small net CT from decane to water, the extended interface displays a systematic opposite electronic asymmetry, yielding a net CT from water to the hydrocarbon phase producing an average surface charge density of ∼0.003 e– nm–2 on the oil phase. This imbalance is accompanied by much larger intraphase self-polarization, particularly within the hydrocarbon phase, demonstrating that collective many-body polarization dominates the interfacial electronic response.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"2 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149226501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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