Journal of Chemical Theory and Computation最新文献_第8页

Reconstruction of the On-Top Two-Electron Density from Natural Orbitals and Their Occupation Numbers.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 DOI: 10.1021/acs.jctc.5c00024

Jerzy Cioslowski, Krzysztof Strasburger

引用次数: 0

Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-14 DOI: 10.1021/acs.jctc.5c00062

Cheng-Han Li, Mehmet Cagri Kaymak, Maksim Kulichenko, Nicholas Lubbers, Benjamin T Nebgen, Sergei Tretiak, Joshua Finkelstein, Daniel P Tabor, Anders M N Niklasson

{"title":"Shadow Molecular Dynamics with a Machine Learned Flexible Charge Potential.","authors":"Cheng-Han Li, Mehmet Cagri Kaymak, Maksim Kulichenko, Nicholas Lubbers, Benjamin T Nebgen, Sergei Tretiak, Joshua Finkelstein, Daniel P Tabor, Anders M N Niklasson","doi":"10.1021/acs.jctc.5c00062","DOIUrl":"10.1021/acs.jctc.5c00062","url":null,"abstract":"We present an extended Lagrangian shadow molecular dynamics scheme with an interatomic Born-Oppenheimer potential determined by the relaxed atomic charges of a second-order charge equilibration model. To parametrize the charge equilibration model, we use machine learning with neural networks to determine the environment-dependent electronegativities and chemical hardness parameters for each atom, in addition to the charge-independent energy and force terms. The approximate shadow molecular dynamics potential in combination with the extended Lagrangian formulation improves the numerical stability and reduces the number of Coulomb potential calculations required to evaluate accurate conservative forces. We demonstrate efficient and accurate simulations with excellent long-term stability of the molecular dynamics trajectories. The significance of choosing fixed or environment-dependent electronegativities and chemical hardness parameters is evaluated. Finally, we compute the infrared spectrum of molecules via the dipole autocorrelation function and compare to experiments to highlight the accuracy of the shadow molecular dynamics scheme with a machine learned flexible charge potential.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"3658-3675"},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143629981","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}

引用次数: 0

Diabatic States of Charge Transfer with Constrained Charge Equilibration.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-20 DOI: 10.1021/acs.jctc.4c01604

Sohang Kundu, Hong-Zhou Ye, Timothy C Berkelbach

{"title":"Diabatic States of Charge Transfer with Constrained Charge Equilibration.","authors":"Sohang Kundu, Hong-Zhou Ye, Timothy C Berkelbach","doi":"10.1021/acs.jctc.4c01604","DOIUrl":"10.1021/acs.jctc.4c01604","url":null,"abstract":"Charge transfer (CT) processes that are electronically nonadiabatic are ubiquitous in chemistry, biology, and materials science, but their theoretical description requires diabatic states or adiabatic excited states. For complex systems, these latter states are more difficult to calculate than the adiabatic ground state. Here, we propose a simple method to obtain diabatic states, including energies and charges, by constraining the atomic charges within the charge equilibration framework. For two-state systems, the exact diabatic coupling can be determined, from which the adiabatic excited-state energy can also be calculated. The method can be viewed as an affordable alternative to constrained density functional theory (CDFT), and so we call it constrained charge equilibration (CQEq). We test the CQEq method on the anthracene-tetracyanoethylene CT complex and the reductive decomposition of ethylene carbonate on a lithium metal surface. We find that CQEq predicts diabatic energies, charges, and adiabatic excitation energies in good agreement with CDFT, and we propose that CQEq is promising for combination with machine learning force fields to study nonadiabatic CT in the condensed phase.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"3545-3551"},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668484","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}

引用次数: 0

Analytic Computation of Vibrational Circular Dichroism Spectra Using Second-Order Møller-Plesset Perturbation Theory.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-25 DOI: 10.1021/acs.jctc.5c00047

Brendan M Shumberger, Kirk C Pearce, T Daniel Crawford

引用次数: 0

Integrating Solvent Effects into the Prediction of Kinetic Constants Using a COSMO-Based Equation of State.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-25 DOI: 10.1021/acs.jctc.5c00133

Francisco Paes, Gabriel de Souza Batalha, Fabiola Citrangolo Destro, René Fournet, Romain Privat, Jean-Noël Jaubert, Baptiste Sirjean

{"title":"Integrating Solvent Effects into the Prediction of Kinetic Constants Using a COSMO-Based Equation of State.","authors":"Francisco Paes, Gabriel de Souza Batalha, Fabiola Citrangolo Destro, René Fournet, Romain Privat, Jean-Noël Jaubert, Baptiste Sirjean","doi":"10.1021/acs.jctc.5c00133","DOIUrl":"10.1021/acs.jctc.5c00133","url":null,"abstract":"While kinetic generators produce thermo-kinetic data for detailed gas-phase kinetic models, adapting these models for liquid-phase applications poses challenges due to the need for solvent-dependent thermodynamic properties. To bridge this gap, solvation energies are used to incorporate solvent effects into gas-phase thermo-kinetic data. However, such an adaptation depends on calculating liquid-phase data of unconventional solutes such as free radicals and transition states, which are not accessible with classical equations of states. To address this issue, this work proposes a flexible framework based on an equation of state that integrates all the latest advances of this model family and is called the tc-PR EoS. Combined with a quantum-based continuum solvation model (COSMO-RS) through an advanced mixing rule, the proposed model is made predictive by employing group contribution methods to estimate the pure compound input parameters required to perform thermodynamic calculations with the model. These parameters can be calculated for closed-shell molecules, free radicals, and transition states, with an average deviation of less than 10% with respect to the benchmark database containing experimental data as well as data obtained from quantum-based calculations and QSPR-type correlations. The tc-PR/COSMO-RS model is able to predict the solvation free energies of activation for H-abstraction reactions with an accuracy of approximately 0.2 kcal/mol, offering a high-throughput and accurate solution for integrating solvation effects into detailed kinetic models in the liquid phase.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"3625-3648"},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699103","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}

引用次数: 0

Bridging the Gap between Transformer-Based Neural Networks and Tensor Networks for Quantum Chemistry.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-02 DOI: 10.1021/acs.jctc.4c01703

Bowen Kan, Yingqi Tian, Yangjun Wu, Yunquan Zhang, Honghui Shang

{"title":"Bridging the Gap between Transformer-Based Neural Networks and Tensor Networks for Quantum Chemistry.","authors":"Bowen Kan, Yingqi Tian, Yangjun Wu, Yunquan Zhang, Honghui Shang","doi":"10.1021/acs.jctc.4c01703","DOIUrl":"https://doi.org/10.1021/acs.jctc.4c01703","url":null,"abstract":"The neural network quantum state (NNQS) method has demonstrated promising results in ab initio quantum chemistry, achieving remarkable accuracy in molecular systems. However, efficient calculation of systems with large active spaces remains challenging. This study introduces a novel approach that bridges tensor network states with the transformer-based NNQS-Transformer (QiankunNet) to enhance accuracy and convergence for systems with relatively large active spaces. By transforming tensor network states into active space configuration interaction type wave functions, QiankunNet achieves accuracy surpassing both the pretraining density matrix renormalization group (DMRG) results and traditional coupled cluster methods, particularly in strongly correlated regimes. We investigate two configuration transformation methods: the sweep-based direct conversion (Conv.) method and the entanglement-driven genetic algorithm (EDGA) method, with Conv. showing superior efficiency. The effectiveness of this approach is validated on H2O with a large active space (10e, 24o) in the cc-pVDZ basis set, demonstrating an efficient routine between DMRG and QiankunNet and also offering a promising direction for advancing quantum state representation in complex molecular systems.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"21 7","pages":"3426-3439"},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802030","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}

引用次数: 0

Efficient Hybrid-Functional-Based Force and Stress Calculations for Periodic Systems with Thousands of Atoms.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-20 DOI: 10.1021/acs.jctc.4c01635

Peize Lin, Yuyang Ji, Lixin He, Xinguo Ren

{"title":"Efficient Hybrid-Functional-Based Force and Stress Calculations for Periodic Systems with Thousands of Atoms.","authors":"Peize Lin, Yuyang Ji, Lixin He, Xinguo Ren","doi":"10.1021/acs.jctc.4c01635","DOIUrl":"10.1021/acs.jctc.4c01635","url":null,"abstract":"We present an efficient linear-scaling algorithm for evaluating the analytical force and stress contributions derived from the exact-exchange energy, a key component in hybrid functional calculations. The algorithm, working equally well for molecular and periodic systems, is formulated within the framework of numerical atomic orbital (NAO) basis sets and takes advantage of the localized resolution-of-identity (LRI) technique for treating the two-electron Coulomb repulsion integrals. The linear-scaling behavior is realized by fully exploiting the sparsity of the expansion coefficients resulting from the strict locality of the NAOs and the LRI ansatz. Our implementation is massively parallel, and enables efficient structural relaxation based on hybrid density functionals for bulk materials containing thousands of atoms. In this work, we will present a detailed description of our algorithm and benchmark the performance of our implementation using illustrating examples. By optimizing the structures of the pristine and doped halide perovskite material CsSnI3 with different functionals, we find that in the presence of lattice strain, hybrid functionals provide a more accurate description of the stereochemical expression of the lone pair.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"3394-3409"},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668485","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}

引用次数: 0

PyCPET─Computing Heterogeneous 3D Protein Electric Fields and Their Dynamics.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 DOI: 10.1021/acs.jctc.5c00138

Pujan Ajmera, Santiago Vargas, Shobhit S Chaturvedi, Matthew Hennefarth, Anastassia N Alexandrova

{"title":"PyCPET─Computing Heterogeneous 3D Protein Electric Fields and Their Dynamics.","authors":"Pujan Ajmera, Santiago Vargas, Shobhit S Chaturvedi, Matthew Hennefarth, Anastassia N Alexandrova","doi":"10.1021/acs.jctc.5c00138","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00138","url":null,"abstract":"Electrostatic preorganization is an exciting mode to understand the catalytic function of enzymes, yet limited tools exist to computationally analyze it. In particular, no methods exist to interpret the geometry, dynamics, and fundamental components of 3D electric fields, E⃗(r), in protein active sites. To address this, we present PyCPET (Python Computation of Electric Field Topologies), a comprehensive, open-source toolbox to analyze E⃗(r) in enzymes. We designed it around computational efficiency and user friendliness with both CPU- and GPU-accelerated codes. Our aim is to provide a set of functions for rich, descriptive analysis of enzyme systems including dynamics, benchmarking, distribution of streamlines analysis in 3D E⃗(r), computation of point E⃗(r), principal component analysis, and 3D E⃗(r) visualization. Finally, we demonstrate its versatility by exploring the nature of electrostatic preorganization and dynamics in three cases: Cytochrome C, Co-substituted Liver Alcohol Dehydrogenase, and HIV Protease. These test systems, along with previous work, establish PyCPET as an essential toolkit for the in-depth analysis and visualization of electric fields in enzymes, unlocking new avenues for understanding electrostatic contributions to enzyme catalysis.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809994","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}

引用次数: 0

Correlated Flat-Bottom Elastic Network Model for Improved Bond Rearrangement in Reaction Paths.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-19 DOI: 10.1021/acs.jctc.4c01549

Shin-Ichi Koda, Shinji Saito

{"title":"Correlated Flat-Bottom Elastic Network Model for Improved Bond Rearrangement in Reaction Paths.","authors":"Shin-Ichi Koda, Shinji Saito","doi":"10.1021/acs.jctc.4c01549","DOIUrl":"10.1021/acs.jctc.4c01549","url":null,"abstract":"This study introduces correlated flat-bottom elastic network model (CFB-ENM), an extension of our recently developed flat-bottom elastic network model (FB-ENM) for generating plausible reaction paths, i.e., collision-free paths preserving nonreactive parts. While FB-ENM improved upon the widely used image-dependent pair potential (IDPP) by addressing unintended structural distortion and bond breaking, it still struggled with regulating the timing of series of bond breaking and formation. CFB-ENM overcomes this limitation by incorporating structure-based correlation terms. These terms impose constraints on pairs of atom pairs, ensuring immediate formation of new bonds after breaking of existing bonds. Using the direct MaxFlux method, we generated paths for 121 reactions involving main group elements and 35 reactions involving transition metals. We found that CFB-ENM significantly improves reaction paths compared to FB-ENM. CFB-ENM paths exhibited lower maximum DFT energies along the paths in most reactions, with nearly half showing significant energy reductions of several tens of kcal/mol. In the few cases where CFB-ENM yielded higher energy paths, most increases were below 10 kcal/mol. We also confirmed that CFB-ENM reduces computational costs in subsequent precise reaction path or transition state searches compared to FB-ENM. An implementation of CFB-ENM based on the Atomic Simulation Environment is available on GitHub for use in computational chemistry research.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"3513-3522"},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661669","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}

引用次数: 0

An Investigation of Physics Informed Neural Networks to Solve the Poisson-Boltzmann Equation in Molecular Electrostatics.

IF 5.7 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-25 DOI: 10.1021/acs.jctc.4c01747

Martín A Achondo, Jehanzeb H Chaudhry, Christopher D Cooper

{"title":"An Investigation of Physics Informed Neural Networks to Solve the Poisson-Boltzmann Equation in Molecular Electrostatics.","authors":"Martín A Achondo, Jehanzeb H Chaudhry, Christopher D Cooper","doi":"10.1021/acs.jctc.4c01747","DOIUrl":"10.1021/acs.jctc.4c01747","url":null,"abstract":"Physics-informed neural networks (PINN) is a machine learning (ML)-based method to solve partial differential equations that has gained great popularity due to the fast development of ML libraries in the past few years. The Poisson-Boltzmann equation (PBE) is widely used to model mean-field electrostatics in molecular systems, and in this work we present a detailed investigation of the use of PINN to solve the linear PBE. Starting from a multidomain PINN for the linear PBE with an interface, we assess the importance of incorporating different features into the neural network architecture. Our findings indicate that the most accurate architecture utilizes input and output scaling layers, a random Fourier features layer, trainable activation functions, and a loss balancing algorithm. The accuracy of our implementation is on the order of 10-2-10-3, which is similar to previous work using PINN to solve other differential equations. We also explore the possibility of incorporating experimental information into the model, and discuss challenges and future work, especially regarding the nonlinear PBE. We are providing an open-source implementation to easily perform computations from a PDB file. We hope this work will motivate application scientists into using PINN to study molecular electrostatics, as ML technology continues to evolve at a high pace.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"3726-3744"},"PeriodicalIF":5.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699099","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}

引用次数: 0