Targeted TPS Shooting Using Computer Vision to Generate Ensemble of Trajectories.

IF 5.5 1区化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2025-04-08 Epub Date: 2025-03-17 DOI:10.1021/acs.jctc.4c01725

Kseniia Korchagina, Steven D Schwartz

引用次数: 0

Abstract

This study presents a transition path sampling (TPS) procedure to create an ensemble of trajectories describing a chemical transformation from a reactant to a product state, augmented with a computer vision technique. A 3D convolutional neural network (CNN) sorts the slices of the TPS trajectories into reactant or product state categories, which aids in automatically accepting or rejecting a newly generated trajectory. Furthermore, information about the geometrical configuration of each slice enables one to calculate the percentage of reactant and product states within a specific shooting range. These statistics are used to determine the most appropriate shooting range and, if needed, to improve a shooting acceptance rate. To test the automated 3D CNN TPS technique, we applied it to collect an ensemble of the transition paths for the rate-limiting step of the Morita-Bayliss-Hillman (MBH) reaction.

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利用计算机视觉生成轨迹集合的目标TPS射击。

本研究提出了一个过渡路径采样（TPS）程序，以创建描述从反应物到产物状态的化学转化的轨迹集合，并辅以计算机视觉技术。3D卷积神经网络（CNN）将TPS轨迹切片分类为反应物或产物状态类别，这有助于自动接受或拒绝新生成的轨迹。此外，关于每个切片的几何结构的信息使人们能够计算在特定射击范围内的反应物和产物状态的百分比。这些统计数据用于确定最合适的射击范围，并在需要时提高射击合格率。为了测试自动3D CNN TPS技术，我们将其应用于收集morita - bayless - hillman （MBH）反应限速步骤的过渡路径集合。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemical Theory and Computation 化学-物理：原子、分子和化学物理

CiteScore

9.90

自引率

16.40%

发文量

568

审稿时长

1 months

期刊介绍： The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.