{"id":"W3162926955","doi":"10.48550/arxiv.2105.03902","title":"Learning Gradient Fields for Molecular Conformation Generation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Computer science; Langevin dynamics; Molecular dynamics; Translation (biology); Algorithm; Field (mathematics); Statistical physics; Force field (fiction); Artificial intelligence; Physics; Computational chemistry; Chemistry; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002063761,0.001158091,0.001546298,0.001489678,0.0006734068,0.001218407,0.00250685,0.00199314,0.002680353],"category_scores_gemma":[0.008188313,0.0007957618,0.0008459041,0.001283988,0.001673454,0.002265691,0.001826977,0.002488359,0.001114866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001523849,"about_ca_system_score_gemma":0.001270765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003954829,"about_ca_topic_score_gemma":0.00406019,"domain_scores_codex":[0.9992746,0.0002486925,0.00003013045,0.0001944208,0.0001775319,0.00007467265],"domain_scores_gemma":[0.9968404,0.001988797,0.0002628398,0.0003428722,0.0003844622,0.0001807432],"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":[0.0001406886,0.000113592,0.001820445,0.00009700909,0.00005648586,0.00008348267,0.00007363991,0.7927399,0.002256584,0.0273994,0.004633904,0.1705849],"study_design_scores_gemma":[0.000008803781,0.00001385918,0.0000533016,0.000004265021,0.000001959444,0.000008635387,0.000002230232,0.9861251,0.0003371603,0.01317068,0.0002694077,0.000004562466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02255879,0.0006560488,0.9728503,0.0005839083,0.0000771235,0.00006160161,0.0001709458,0.001892419,0.001148808],"genre_scores_gemma":[0.6406536,0.0007097347,0.3500592,0.0008107316,0.00026824,0.0003059522,0.001718127,0.0006780094,0.004796399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003954829,"threshold_uncertainty_score":0.01105636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05483604566554429,"score_gpt":0.20320592723399,"score_spread":0.1483698815684457,"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."}}