{"id":"W4405561159","doi":"10.26434/chemrxiv-2024-shw8x","title":"Employing the active learning strategy to construct full-dimensional intermolecular potential energy surfaces within spectroscopic accuracy","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial neural network; Sampling (signal processing); Dimension (graph theory); Range (aeronautics); Mean squared error; Test set; Algorithm; Computer science; Energy (signal processing); Radial basis function; Mathematics; Mathematical optimization; Artificial intelligence; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001317145,0.0006877625,0.0006645834,0.0001875478,0.0005180244,0.00160403,0.001736202,0.0003094445,0.001834994],"category_scores_gemma":[0.0007982301,0.0005105927,0.0002111509,0.0003172023,0.0005314107,0.0001516244,0.004084768,0.001583946,0.0007173416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002062979,"about_ca_system_score_gemma":0.0006293728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005607174,"about_ca_topic_score_gemma":0.00003386244,"domain_scores_codex":[0.9952853,0.0006203175,0.000760512,0.001625714,0.0009297992,0.0007783222],"domain_scores_gemma":[0.9978138,0.000288968,0.0005820118,0.0008843207,0.0002008277,0.0002300961],"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":[0.00006755935,0.00001538192,0.0000272942,0.0001082931,0.0000338691,0.00008191657,0.0005673887,0.2473637,0.7505482,0.0007471035,0.0001836199,0.0002556758],"study_design_scores_gemma":[0.0002028899,0.0002086033,0.000324651,0.000652067,0.0001075078,0.0001242894,0.0003558854,0.0406583,0.9477509,0.008549676,0.0002443914,0.0008208106],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.990005,0.0003115801,0.00242533,0.0007323754,0.004851725,0.0004070246,0.00001613528,0.0004276683,0.0008231536],"genre_scores_gemma":[0.9939638,0.000009149943,0.004439062,0.0002442019,0.0005138403,0.00009386034,0.00003558188,0.00009045054,0.0006100541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2067054,"threshold_uncertainty_score":0.9997346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01317450374547896,"score_gpt":0.2809637992815167,"score_spread":0.2677892955360377,"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."}}