{"id":"W4387427529","doi":"10.48550/arxiv.2310.03121","title":"OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Agencia Estatal de Investigación; Engineering and Physical Sciences Research Council; York University; Damon Runyon Cancer Research Foundation; National Institutes of Health; National Science Foundation","keywords":"Computer science; Molecular dynamics; CUDA; Interface (matter); Artificial intelligence; Computational science; Molecular machine; Parallel computing; Chemistry; Nanotechnology; Materials science; Computational chemistry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001826489,0.001499857,0.00304509,0.0008820082,0.001444067,0.001685941,0.006529731,0.003424282,0.02938498],"category_scores_gemma":[0.004909115,0.001346626,0.001786144,0.001590559,0.0009178318,0.00210102,0.002846242,0.005406721,0.006031938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001523833,"about_ca_system_score_gemma":0.002403706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005306547,"about_ca_topic_score_gemma":0.005134136,"domain_scores_codex":[0.998892,0.0003937916,0.00005387363,0.0001464548,0.0003897769,0.0001242335],"domain_scores_gemma":[0.9987044,0.000655353,0.00005327256,0.0001848742,0.0002548865,0.00014719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000888344,0.0004570527,0.001681782,0.001208433,0.0005089925,0.0005765815,0.0005608071,0.6292455,0.006271545,0.1470432,0.1458457,0.06571207],"study_design_scores_gemma":[0.0002773417,0.00004034157,0.000148786,0.00003825683,0.00001995804,0.00004291128,0.00001726611,0.9440562,0.002221301,0.02408443,0.02901097,0.00004234297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07146993,0.003002908,0.7543792,0.002829259,0.002115512,0.0009044703,0.02239143,0.07520425,0.06770304],"genre_scores_gemma":[0.235183,0.001745951,0.6815124,0.001109716,0.0004440962,0.007030224,0.02062831,0.03072849,0.0216178],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02938498,"threshold_uncertainty_score":0.09830254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05121974540552007,"score_gpt":0.209430954301388,"score_spread":0.1582112088958679,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}