{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005094445,0.0001096894,0.0003634355,0.00003891347,0.0001884391,0.0000766595,0.0003896962,0.00002524185,0.00001962368],"category_scores_gemma":[0.00128305,0.00006204057,0.0001027372,0.0003598167,0.0001479335,0.00006385148,0.0001588709,0.0001419325,0.000001014098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004811578,"about_ca_system_score_gemma":0.00003976521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008836899,"about_ca_topic_score_gemma":0.000001738518,"domain_scores_codex":[0.9985102,0.0004303567,0.000462034,0.0001364018,0.0003043486,0.0001566575],"domain_scores_gemma":[0.9976846,0.001255282,0.0005276122,0.0002141104,0.0002752762,0.00004313011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001605138,0.00003402126,0.00009997511,0.00005292413,0.00005160911,1.614506e-7,0.0002265891,0.09974264,0.8993837,0.0000146983,0.000005071182,0.0002280804],"study_design_scores_gemma":[0.0002041215,0.00004311375,0.001928066,0.00004497231,0.0003964732,0.000004586085,0.00007407241,0.2111526,0.7855195,0.0005696157,0.000005800961,0.00005710215],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716949,0.00003321284,0.02737288,0.0006795137,0.00005328083,0.00009391791,0.000007847699,0.000007035953,0.00005735942],"genre_scores_gemma":[0.9982275,0.000001583824,0.001553012,0.00004410214,0.0001030324,5.076722e-7,8.103689e-7,0.000005397937,0.00006401067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1138642,"threshold_uncertainty_score":0.2529941,"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."}}