{"title":"利用计算机视觉生成轨迹集合的目标TPS射击。","authors":"Kseniia Korchagina, Steven D Schwartz","doi":"10.1021/acs.jctc.4c01725","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":"3353-3359"},"PeriodicalIF":5.7000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Targeted TPS Shooting Using Computer Vision to Generate Ensemble of Trajectories.\",\"authors\":\"Kseniia Korchagina, Steven D Schwartz\",\"doi\":\"10.1021/acs.jctc.4c01725\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":45,\"journal\":{\"name\":\"Journal of Chemical Theory and Computation\",\"volume\":\" \",\"pages\":\"3353-3359\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Chemical Theory and Computation\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.jctc.4c01725\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/3/17 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Theory and Computation","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jctc.4c01725","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/17 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Targeted TPS Shooting Using Computer Vision to Generate Ensemble of Trajectories.
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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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.
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