{"id":"W2932948170","doi":"10.1039/c9cp01883b","title":"Bayesian machine learning for quantum molecular dynamics","year":2019,"lang":"en","type":"article","venue":"Physical Chemistry Chemical Physics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver Biotech (Canada); University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bayesian probability; Quantum; Hamiltonian (control theory); Quantum dynamics; Dynamical systems theory; Bayesian inference; Quantum machine learning; Molecular dynamics; Complement (music)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004405759,0.001155945,0.001483721,0.001306043,0.0009012865,0.002143184,0.001900083,0.002299822,0.004074946],"category_scores_gemma":[0.01587285,0.0007219903,0.001273876,0.001571842,0.002642989,0.002926675,0.002254224,0.004763734,0.0009985175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002776552,"about_ca_system_score_gemma":0.001845942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004840519,"about_ca_topic_score_gemma":0.003352559,"domain_scores_codex":[0.9978247,0.001248034,0.00007809445,0.0002186775,0.0005368425,0.000093696],"domain_scores_gemma":[0.9928712,0.005759849,0.0003222343,0.0004504631,0.0004480103,0.0001482025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002287683,0.00002954951,0.0003340378,0.0001498763,0.00005131177,0.00004438106,0.0000752728,0.2042217,0.0003334267,0.7664312,0.002275243,0.02603123],"study_design_scores_gemma":[0.000007005238,0.00001129761,0.00006298344,0.00003127737,0.000005197066,0.00001241921,0.000006697385,0.6004303,0.0001163708,0.3955962,0.003707051,0.00001325249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001698697,0.003037076,0.9877208,0.001845337,0.0001774255,0.0000405311,0.0000970064,0.0001928754,0.005190275],"genre_scores_gemma":[0.2935869,0.01386158,0.6770557,0.001525986,0.001927814,0.0008407442,0.0006252589,0.0003821712,0.01019382],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004840519,"threshold_uncertainty_score":0.02330011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00566848391634287,"score_gpt":0.243632111886072,"score_spread":0.2379636279697291,"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."}}