{"id":"W2790885656","doi":"10.1080/00268976.2018.1429687","title":"Constructing high-accuracy intermolecular potential energy surface with multi-dimension Morse/Long-Range model","year":2018,"lang":"en","type":"article","venue":"Molecular Physics","topic":"Advanced Chemical Physics Studies","field":"Physics and Astronomy","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"van der Waals force; Range (aeronautics); Morse potential; Dimension (graph theory); Potential energy; Morse code; Statistical physics; Intermolecular force; Function (biology); Energy (signal processing); Differentiable function; Surface (topology); Morse theory; Physics; Computer science; Quantum mechanics; Mathematics; Materials science; Mathematical analysis; Pure mathematics; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00003284387,0.0004401303,0.0004066935,0.00002155192,0.00023585,0.00005473682,0.0002655782,0.00005256954,0.00001921243],"category_scores_gemma":[0.000005989202,0.0004148076,0.0001676822,0.0002447221,0.000420706,0.0002318409,0.000305985,0.000240483,0.00002401466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006022127,"about_ca_system_score_gemma":0.00005247124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001362721,"about_ca_topic_score_gemma":0.000003414564,"domain_scores_codex":[0.9982018,0.00004220551,0.0002675602,0.0006091642,0.0003412038,0.0005380114],"domain_scores_gemma":[0.9987374,0.0000294989,0.0002291293,0.0005517848,0.0003217862,0.0001303972],"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":[0.0001448246,0.0005679027,0.002546404,0.00003561655,0.0007770095,0.00005893916,0.0003681362,0.09511879,0.7892953,0.09926133,0.0001440511,0.01168173],"study_design_scores_gemma":[0.001477584,0.00009890158,0.0000286125,0.00007056631,0.00014668,0.000003403774,0.0001755318,0.09157244,0.854539,0.05115834,0.00001944798,0.0007095017],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4326288,0.00002512063,0.5662688,0.00002743756,0.00006466602,0.00009561038,0.00002630426,0.0000569292,0.0008063456],"genre_scores_gemma":[0.9610543,0.000001003876,0.03819997,0.0001139474,0.0003213552,0.00001843539,0.000096721,0.000100697,0.00009357595],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5284255,"threshold_uncertainty_score":0.9998304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00754191023328165,"score_gpt":0.232890485859909,"score_spread":0.2253485756266273,"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."}}