{"id":"W4414962227","doi":"10.1021/acs.jpcb.5c04626","title":"Using Time Dependent Rate Analysis to Evaluate the Quality of Machine Learned Reaction Coordinates for Biasing and Computing Kinetics","year":2025,"lang":"en","type":"article","venue":"The Journal of Physical Chemistry B","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; York University; Simons Foundation","keywords":"Metric (unit); Inverse; Metadynamics; Dimensionless quantity; Kinetic energy; Exponential function; Reaction coordinate; Biasing","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":[0.004038164,0.001418686,0.0008184123,0.001003369,0.0004323726,0.00133706,0.001365779,0.0009112495,0.001605441],"category_scores_gemma":[0.0158518,0.0004013123,0.0006278405,0.0008028529,0.0006419821,0.001875345,0.0006408509,0.001855541,0.0005747818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001360122,"about_ca_system_score_gemma":0.00156221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00422616,"about_ca_topic_score_gemma":0.003087958,"domain_scores_codex":[0.9992673,0.0001903042,0.0000652875,0.000185273,0.0002387289,0.00005313514],"domain_scores_gemma":[0.9951246,0.002861527,0.0006829076,0.0006253999,0.0006043323,0.0001012269],"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":[0.0004467579,0.000154449,0.01236139,0.0004013804,0.0002034382,0.0001146088,0.000270048,0.8457162,0.05617919,0.009121821,0.001230027,0.07380065],"study_design_scores_gemma":[0.000006433355,0.00005483062,0.000874053,0.000009014011,0.0000121752,0.00002024898,0.00001799852,0.9752558,0.02238253,0.0009779999,0.0003671387,0.00002179331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2828551,0.0008051816,0.7066543,0.0003390119,0.00008986134,0.0000919356,0.0003793887,0.006260982,0.002524277],"genre_scores_gemma":[0.8132612,0.0004415229,0.1837605,0.00006510774,0.00001618436,0.0001696803,0.0006498485,0.0007937857,0.0008421515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00422616,"threshold_uncertainty_score":0.02135617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04114930151712823,"score_gpt":0.384043211434453,"score_spread":0.3428939099173248,"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."}}