[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118016-en":3,"doc-seo-118016-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118016,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","OpenMM 8 - Molecular Dynamics Simulation with Machine Learning Potentials - Overview","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 the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation to compute forces and energy, while a higher-level interface enables modeling of target molecules using general purpose pretrained potential functions. Optimized CUDA kernels and custom PyTorch operations improve simulation speed. The approach is demonstrated on CDK8 and the GFP chromophore in water, showing improved accuracy with only modest added cost.","Edinburgh Research Explorer  \nOpenMM 8  \nCitation for published version:  \nEastman, P, Galvelis, R, Peláez, RP, Abreu, CRA, Farr, SE, Gallicchio, E, Gorenko, A, Henry, MM, Hu, F, Huang, J, Krämer, A, Michel, J, Mitchell, JA, Pande, VS, Rodrigues, JPGLM, Rodriguez-Guerra, J, Simmonett, AC, Singh, S, Swails, J, Turner, P, Wang, Y, Zhang, I, Chodera, JD, De Fabritiis, G & Markland, TE 2023, 'OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials', Journal of Physical Chemistry B, vol. 128, no. 1, pp. 109-116. [https://doi.org/10.1021/acs.jpcb.3c06662](https://doi.org/10.1021/acs.jpcb.3c06662)  \nDigital Object Identifier (DOI):  \n10.1021/acs.jpcb.3c06662  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPeer reviewed version  \nPublished In:  \nJournal of Physical Chemistry B  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 11. May. 2024  \nOpenMM 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 Athen","cbCaikYoBpbRDeEB","https://ap.wps.com/l/cbCaikYoBpbRDeEB","pdf",2422089,1,17,"English","en",105,"# Abstract\n# Introduction\n## Molecular simulation accuracy vs cost\n## Force fields and machine learning potentials","[{\"question\":\"What does OpenMM 8 add for machine learning-based molecular simulations?\",\"answer\":\"OpenMM 8 introduces features that enable machine learning potentials to be integrated into molecular dynamics simulations, including support for arbitrary PyTorch models to compute forces and energy.\"},{\"question\":\"How can users apply pretrained potential functions in OpenMM 8?\",\"answer\":\"A higher-level interface allows users to model molecules of interest with general purpose, pretrained potential functions, streamlining setup for target systems.\"},{\"question\":\"What performance improvements are reported and how are they demonstrated?\",\"answer\":\"Optimized CUDA kernels and custom PyTorch operations improve simulation speed. The capabilities are demonstrated using simulations of CDK8 and the GFP chromophore in water.\"}]","OpenMM 8 - Molecular Dynamics Simulation with Machine Learning Potentials - Overview | PDF",1785680766,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"openmm-8-molecular-dynamics-simulation-with-machine-learning-potentials-overview","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/openmm-8-molecular-dynamics-simulation-with-machine-learning-potentials-overview/118016/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does OpenMM 8 add for machine learning-based molecular simulations?","Question",{"text":76,"@type":77},"OpenMM 8 introduces features that enable machine learning potentials to be integrated into molecular dynamics simulations, including support for arbitrary PyTorch models to compute forces and energy.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How can users apply pretrained potential functions in OpenMM 8?",{"text":81,"@type":77},"A higher-level interface allows users to model molecules of interest with general purpose, pretrained potential functions, streamlining setup for target systems.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance improvements are reported and how are they demonstrated?",{"text":85,"@type":77},"Optimized CUDA kernels and custom PyTorch operations improve simulation speed. 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