[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118018-en":3,"doc-seo-118018-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118018,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",6,"Technology","OpenMM 8 - Molecular Dynamics Simulation with Machine Learning Potentials","Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support machine learning potentials. Arbitrary PyTorch models can be added to compute forces and energy, while a higher-level interface helps model target molecules with pretrained potential functions. Optimized CUDA kernels and custom PyTorch operations significantly accelerate simulations, demonstrated on CDK8 and the GFP chromophore in water.","OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials  \nPeter Eastman1*, Raimondas Galvelis2,3 , Raúl P. Peláez3 , Charlles R. A. Abreu4,5 , Stephen E. Farr6 , Emilio Gallicchio7,8 , Anton Gorenko9 , Michael M. Henry 10 , Frank Hu1 , Jing Huang 11 , Andreas Krämer12 , Julien Michel6 , Joshua A. Mitchell13 , Vijay S. Pande14,15 , João PGLM Rodrigues 15 , Jaime Rodriguez-Guerra16 , Andrew C. Simmonett17 , Sukrit Singh10 , Jason Swails18 , Philip Turner19 , Yuanqing Wang20 , Ivy Zhang 10,21 , John D.  \nChodera10 , Gianni De Fabritiis2,3,22 , Thomas E. Markland 1  \n1 Department of Chemistry, Stanford University, Stanford, CA 94305, USA 2Acellera Labs, C Dr Trueta 183, 08005, Barcelona, Spain 3Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003, Barcelona, Spain  \n4Chemical Engineering Department, School of Chemistry, Federal University of Rio de Janeiro, Rio de Janeiro  \n68542, Brazil  \n5 Redesign Science Inc. , 180 Varick St. , New York, NY 10014, USA 6 EaStCHEM School of Chemistry, University of Edinburgh, EH9 3FJ, United Kingdom 7 Department of Chemistry and Biochemistry, Brooklyn College of the City University of New York, NY, USA 8 Ph. D. Program in Chemistry and Ph. D. Program in Biochemistry, The Graduate Center of the City University of  \nNew York, New York, NY, USA  \n9Stream HPC, Koningin Wilhelminaplein 1-40601, 1062 HG Amsterdam, Netherlands 10Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer  \nCenter, New York NY 10065, USA  \n11 Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18  \nShilongshan Road, Hangzhou 310024, Zhejiang, China  \n12 Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195 Berlin,  \nGermany  \n13The Open Force Field Initiative, Open Molecular Software Foundation, Davis, CA 95616, USA 14Andreessen Horowitz, 2865 Sand Hill Rd, Menlo Park, CA 94025, USA 15 Department of Structural Biology, Stanford University, Stanford, CA 94305, USA 16Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics, Virchowweg 6, 10117  \nBerlin, Germany  \n17 Laboratory of Computational Biology, National Heart, Lung and Blood Institute, National Institutes of Health,  \nBethesda, MD 20892, USA  \n18 Entos Inc. , 9310 Athena Circle, La Jolla, CA 92037, USA  \n19College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA 20Simons Center for Computational Physical Chemistry and Center for Data Science, New York University, 24  \nWaverly Place, New York, NY 10004, USA  \n21Tri-Institutional PhD Program in Computational Biology and Medicine, Weill Cornell Medical College, Cornell  \nUniversity, New York, NY 10065, USA  \n22ICREA, Passeig Lluis Companys 23, 08010, Barcelona, Spain  \n*Corresponding author ([peastman@stanford.edu](peastman@stanford.edu))  \nAbstract  \nMachine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.  \nIntroduction  \nIn recent years, much work in the field of molecular simulation has focused on ways to produce more accurate results at lower ","cbCaiuSWSS7qbmFE","https://ap.wps.com/l/cbCaiuSWSS7qbmFE","pdf",2293527,1,16,"English","en",105,"# Introduction\n## Machine learning potentials and motivation\n## New features in OpenMM 8","[{\"question\":\"What is the main purpose of OpenMM 8 in this document?\",\"answer\":\"OpenMM 8 is presented as a molecular simulation toolkit that adds new capabilities specifically to support machine learning potentials for improved simulation accuracy and practical cost.\"},{\"question\":\"How can machine learning potentials be integrated into an OpenMM simulation?\",\"answer\":\"The document states that arbitrary PyTorch models can be plugged into a simulation to compute forces and energy, and a higher-level interface can use general pretrained potential functions for modeling target molecules.\"},{\"question\":\"What performance improvements does OpenMM 8 provide for ML-based simulations?\",\"answer\":\"It highlights that a collection of optimized CUDA kernels and custom PyTorch operations greatly improves simulation speed, with demonstrations on systems such as CDK8 and the GFP chromophore in water.\"}]","OpenMM 8 - 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