Journal of Chemical Theory and Computation最新文献_第4页

Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials. 用神经网络电位预测环化反应途径的选择性。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-09 DOI: 10.1021/acs.jctc.5c01161

Nicholas Casetti, Dylan Anstine, Olexandr Isayev, Connor W Coley

引用次数: 0

Hybrid Tensor Network and Neural Network Quantum States for Quantum Chemistry. 量子化学中的混合张量网络和神经网络量子态。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-09 DOI: 10.1021/acs.jctc.5c01228

Zibo Wu, Bohan Zhang, Wei-Hai Fang, Zhendong Li

{"title":"Hybrid Tensor Network and Neural Network Quantum States for Quantum Chemistry.","authors":"Zibo Wu, Bohan Zhang, Wei-Hai Fang, Zhendong Li","doi":"10.1021/acs.jctc.5c01228","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01228","url":null,"abstract":"Neural network quantum states (NQS) have emerged as a powerful and flexible framework for addressing quantum many-body problems. While successful for model Hamiltonians, their application to molecular systems remains challenging for several reasons. In this work, we introduce three innovations to overcome some of the key limitations. (1) We develop a bounded-degree graph recurrent neural network (BDG-RNN) ansatz, which hybridizes the tensor network and neural network states and is more suitable to molecular electronic structure problems. As matrix product states (MPS) can be embedded into this ansatz, good initialization is possible for complex systems. (2) We introduce neural network correlators (NNCs) to further enhance expressivity and improve accuracy, without dramatically modifying the underlying variational Monte Carlo (VMC) optimization framework. Specifically, we introduce two types of restricted Boltzmann machine (RBM)-inspired correlators, namely, cos-RBM and Ising-RBM, which unlike previous correlators, such as Jastrow and real RBM, can adjust the sign structure of the wave function. (3) We introduce a semistochastic algorithm for local energy evaluation, which significantly reduces computational cost while maintaining high accuracy. Combining these advances, we demonstrate that our approaches can achieve chemical accuracy in challenging systems, including the one-dimensional hydrogen chain H50, the iron-sulfur cluster [Fe2S2(SCH3)4]2-, and a three-dimensional 3 × 3 × 2 hydrogen cluster H18. These methods are implemented in an open-source package, PyNQS (https://github.com/Quantum-Chemistry-Group-BNU/PyNQS), to advance NQS methodologies for quantum chemistry.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248960","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

A Novel Classical Density Functional Theory Base on Multibody Potential and Its Application to the Formation of Metal Nanoclusters. 基于多体势的新型经典密度泛函理论及其在金属纳米团簇形成中的应用。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-09 DOI: 10.1021/acs.jctc.5c01070

Fanfeng Ding,Yu Liu

引用次数: 0

Undirected Exploration of Binding Pockets with Flexible Topology. 柔性拓扑绑定口袋的无向探索。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-09 DOI: 10.1021/acs.jctc.5c00825

Fatemeh Fathi Niazi, Seungmin Yoon, Khadim Mbacke, Alex Dickson

{"title":"Undirected Exploration of Binding Pockets with Flexible Topology.","authors":"Fatemeh Fathi Niazi, Seungmin Yoon, Khadim Mbacke, Alex Dickson","doi":"10.1021/acs.jctc.5c00825","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c00825","url":null,"abstract":"A common first step in drug design is virtual high-throughput screening (VHTS), where a large number of potential drug molecules are computationally modeled in a protein binding pocket and filtered down to a smaller set of hits that can be further tested computationally or experimentally. Traditional strategies for VHTS do not account for ligand-induced conformational changes in proteins as they typically rely on a single static structure to represent the protein. This neglects the role of binding entropy and the fact that different ligand molecules can induce slightly different conformations in the protein binding site that significantly affect the assessment of a given molecule's fit. To address this challenge, we have developed a method called \"flexible topology\", where a subset of atoms, typically representing a small molecule ligand, can continuously change their atomic identities, which are encoded by a set of attributes that parametrize the nonbonded interactions. These attributes are all implemented as dynamic variables that have masses and evolve over time using gradients of the energy function. In other words, the attributes feel forces from their surrounding environment and respond accordingly. In this way, by observing a set of flexible topology particles move and change in a ligand-binding site, we can learn the preferences of a binding pocket. Here, we demonstrate how undirected flexible topology simulations can be used to explore ligand-binding sites and reveal the desirable properties of potential ligands. We use the β-2-adrenergic receptor as an illustrative example and compare the properties of flexible topology particle groups with a set of 29 B2AR ligand-bound crystal structures, covering 13 distinct ligands. We also show how the shape- and electrostatics-based virtual screening software \"eon\" from OpenEye can be used to find hits that come as close as possible to mimicking the orientation of our flexible topology atoms.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248933","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

Topological Analysis Reveals Multiple Pathways in Molecular Dynamics. 拓扑分析揭示分子动力学的多种途径。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-09 DOI: 10.1021/acs.jctc.5c00819

Luca Donati, Surahit Chewle, Dominik St Pierre, Vijay Natarajan, Marcus Weber

引用次数: 0

NN-VRCTST: Neural Network Potentials Meet Variable Reaction Coordinate Transition State Theory for the Rate Constant Determination of Barrierless Reactions. NN-VRCTST：神经网络势满足变反应坐标的过渡态理论确定无障碍反应的速率常数。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-09 DOI: 10.1021/acs.jctc.5c01288

Simone Vari,Carlo de Falco,Carlo Cavallotti

{"title":"NN-VRCTST: Neural Network Potentials Meet Variable Reaction Coordinate Transition State Theory for the Rate Constant Determination of Barrierless Reactions.","authors":"Simone Vari,Carlo de Falco,Carlo Cavallotti","doi":"10.1021/acs.jctc.5c01288","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01288","url":null,"abstract":"The determination of rate constants for barrierless reactions poses severe problems from a theoretical perspective. The main challenges concern the proper description of the electronic structure of the reacting system, which may have multireference character, the anharmonicity of the relative motions of the fragments, and the proper definition of the reaction coordinate. The literature state of the art in the context of transition state theory is its variable reaction coordinate implementation (VRC-TST), which overcomes these difficulties in determining the number of transition state ro-vibrational states through a Monte Carlo sampling of the potential energy surface (PES) defined by the relative orientation of the two fragments. Although approaching the accuracy of experiments, VRC-TST requires tens of thousands of single-point energy (SPE) evaluations, thus being computationally demanding. The approach developed in this work, named NN-VRCTST, aims at fitting the PES with physics-inspired artificial neural network (ANN) models to be used as surrogate potentials in VRC-TST simulations. The ANN efficacy is evaluated in the computation of high-pressure limit rate constants for gas-phase barrierless reactions and validated over state-of-the-art VRC-TST simulations. It is shown that the NN-VRCTST tool reaches an accuracy within 20% with respect to VRC-TST simulations performed by using traditional approaches. While lowering the number of SPE needed by at least a factor of 4, the computational framework devised here allows one to decouple ANN training and VRC-TST calculations, enabling the optimization of the SPE evaluations as well as the quality inspection of the employed data points. We believe that the NN-VRCTST approach has the potential to evolve into a robust and computationally efficient framework for performing VRC-TST calculations for barrierless reactions.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"58 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254509","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

Nonorthogonal Configuration Interaction of Constraint-Based Orbital-Optimized Excited States: A Versatile Method for Theoretical Photochemistry. 基于约束的轨道优化激发态的非正交构型相互作用：理论光化学的通用方法。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-08 DOI: 10.1021/acs.jctc.5c01064

Yannick Lemke,Jörg Kussmann,Christian Ochsenfeld

引用次数: 0

Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional. GPU加速DFT交换-相关泛函求值的高效算法。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-08 DOI: 10.1021/acs.jctc.5c01229

Ryan Stocks, Giuseppe M J Barca

{"title":"Efficient Algorithms for GPU Accelerated Evaluation of the DFT Exchange-Correlation Functional.","authors":"Ryan Stocks, Giuseppe M J Barca","doi":"10.1021/acs.jctc.5c01229","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01229","url":null,"abstract":"Kohn-Sham density functional theory (KS-DFT) has become a cornerstone for studying the electronic structure of molecules and materials. Improving algorithmic efficiency through hardware-aware implementations enables application to larger systems and more efficient generation of larger training data sets for machine-learning. In this work, we present a comparative study of four GPU-accelerated algorithms for evaluating the KS-DFT exchange-correlation (XC) potential with an atom-centered Gaussian basis. Two approaches, both leveraging batched dense linear algebra, are found to outperform the others across a suite of molecular benchmarks. We show that batched formation of the XC matrix from the density matrix yields the best performance for large (<math><mo>></mo><mi>O</mi><mrow><mo>(</mo><msup><mn>10</mn><mn>3</mn></msup><mo>)</mo></mrow></math> basis functions), sparse systems such as glycine chains and water clusters. In contrast, for smaller and denser systems such as diamond nanoparticles, especially if employing large basis sets, algorithms that use the underlying molecular orbital coefficients offer superior performance, despite their higher formal scaling. Our implementations deliver speedups of 1.4-5.2× for XC potential evaluation relative to leading GPU-accelerated KS-DFT codes, significantly lowering the computational cost and enabling the routine use of larger integration grids. Finally, we outline directions for continued performance improvements in light of emerging GPU architectures with emphasis on utilizing mixed-precision capabilities.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248971","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

Grand-Canonical Equivariant Neural Potentials for Electrochemical Interfaces. 电化学界面的大规范等变神经电位。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-08 DOI: 10.1021/acs.jctc.5c01381

Jia-Lan Chen,Xin-Ze Qi,Jinze Zhu,Jin Li,Xue-Chun Jiang,Wei-Xue Li,Jin-Xun Liu

{"title":"Grand-Canonical Equivariant Neural Potentials for Electrochemical Interfaces.","authors":"Jia-Lan Chen,Xin-Ze Qi,Jinze Zhu,Jin Li,Xue-Chun Jiang,Wei-Xue Li,Jin-Xun Liu","doi":"10.1021/acs.jctc.5c01381","DOIUrl":"https://doi.org/10.1021/acs.jctc.5c01381","url":null,"abstract":"Electrochemical reactions under constant potential underpin critical processes in energy storage, catalysis, and corrosion but remain challenging to model owing to the voltage insensitivity of conventional machine learning potentials. The lack of a unified framework incorporating grand-canonical constraints into machine-learned models fundamentally limits accurate, scalable simulations of potential-dependent interfacial phenomena. Here, we present a constant-potential, E(3)-equivariant message-passing neural network (CPMPNN) that integrates grand-canonical electronic structure principles with a global excess-charge parameter that is dynamically redistributed via a multihead attention mechanism. The atomic geometry is encoded through a graph neural network that preserves the full symmetry of the Euclidean group in three dimensions (E(3))─including translations, rotations, and reflections. Benchmarking against the grand-canonical DFT confirms that the CPMPNN retains first-principles accuracy while achieving a three-orders-of-magnitude computational speedup. Applied to key electrocatalytic processes─CO dimerization in CO2 reduction and the Volmer step in hydrogen evolution on Cu(100)─CPMPNN captures how the applied potential modulates the reaction thermodynamics, charge distribution, and transition-state structures, providing mechanistic insight into potential-dependent kinetics. By bridging first-principle accuracy with molecular dynamics scalability, CPMPNN provides a transferable framework for operando modeling of electrified interfaces, enabling new mechanistic insights into potential-controlled electrocatalysis.","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":"11 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246752","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

Gaussian-Based Periodic Grand Canonical Density Functional Theory with Implicit Solvation for Computational Electrochemistry. 计算电化学中隐式溶剂化的高斯周期大正则密度泛函理论。

IF 5.5 1区化学

Journal of Chemical Theory and Computation Pub Date : 2025-10-07 DOI: 10.1021/acs.jctc.5c01403

Anton Z Ni, Adam Rettig, Joonho Lee

引用次数: 0