{"id":"W4225270064","doi":"10.1063/5.0089570","title":"Adaptive fitting of potential energy surfaces of small to medium-sized molecules in sum-of-product form: Application to vibrational spectroscopy","year":2022,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Advanced Chemical Physics Studies","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Hanns-Seidel-Stiftung; Koning Boudewijnstichting; Natural Sciences and Engineering Research Council of Canada; Fonds Wetenschappelijk Onderzoek; Fonds De La Recherche Scientifique - FNRS","keywords":"Ab initio; Computation; Potential energy surface; Potential energy; Chemistry; Product (mathematics); Representation (politics); Sampling (signal processing); Relaxation (psychology); Energy (signal processing); Statistical physics; Computational chemistry; Molecular physics; Computational physics; Physics; Atomic physics; Quantum mechanics; Mathematics; Algorithm; Geometry; Optics","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":[0.0007524811,0.0006968091,0.0005396167,0.0005840189,0.0003982013,0.000519543,0.001195607,0.0006167914,0.002387532],"category_scores_gemma":[0.002918113,0.0003280134,0.0005425399,0.0007420088,0.0002638474,0.0006247793,0.0008686438,0.0009620196,0.0004204345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003567313,"about_ca_system_score_gemma":0.0005618522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001005624,"about_ca_topic_score_gemma":0.0007366405,"domain_scores_codex":[0.9997516,0.00008707389,0.0000111397,0.00002699472,0.0001065175,0.00001670661],"domain_scores_gemma":[0.9992858,0.0004298521,0.00004239898,0.0001190181,0.0001013407,0.00002159656],"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":[0.00009478474,0.0001240589,0.001417744,0.0001804402,0.00005780184,0.0001198045,0.0001750395,0.850717,0.02759834,0.01718335,0.001297957,0.1010338],"study_design_scores_gemma":[0.000003591635,0.00001112552,0.0001279359,0.000002719545,0.000002195087,0.00001860014,0.00001056438,0.9943573,0.002468377,0.002572624,0.0004181835,0.000006807417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1040526,0.0001377524,0.8910132,0.0001023325,0.0000228997,0.00008176457,0.0001805943,0.001944614,0.002464185],"genre_scores_gemma":[0.5362412,0.0001543303,0.4604528,0.00003191853,0.00001580691,0.0003125341,0.0005903658,0.0009454275,0.001255734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002387532,"threshold_uncertainty_score":0.007987082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00979072845401887,"score_gpt":0.2417946182976794,"score_spread":0.2320038898436605,"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."}}