{"id":"W4313254213","doi":"10.26434/chemrxiv-2022-mdz85-v2","title":"A Neural Network Potential with Rigorous Treatment of Long-Range Dispersion","year":2022,"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":"Dalhousie University; Memorial University of Newfoundland; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Nvidia","keywords":"Intermolecular force; Dispersion (optics); Artificial neural network; Statistical physics; Range (aeronautics); Test set; Dipole; Ab initio; London dispersion force; Chemistry; Computational chemistry; Physics; Computer science; Quantum mechanics; Materials science; Machine learning; van der Waals force; Molecule","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":[0.0008714308,0.0005105788,0.0006541063,0.000497069,0.0006135944,0.0006052814,0.001543809,0.001256837,0.001423589],"category_scores_gemma":[0.002229382,0.0003738468,0.0005484314,0.0007608407,0.0007352325,0.001309569,0.001235542,0.001312207,0.0003286156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008230547,"about_ca_system_score_gemma":0.001606652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007482386,"about_ca_topic_score_gemma":0.005529042,"domain_scores_codex":[0.9996344,0.0001033851,0.00001406379,0.00003506517,0.0001751811,0.00003793952],"domain_scores_gemma":[0.9995982,0.0001741533,0.00003382891,0.00006120638,0.0001051093,0.00002753796],"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":[0.00001865717,0.00002251897,0.0003401325,0.00004775384,0.00001499043,0.00008887827,0.00002286326,0.9314614,0.002376547,0.05408375,0.00076829,0.01075418],"study_design_scores_gemma":[0.000004012602,0.000005316179,0.00005855668,0.000002681134,0.000001004235,0.000006732551,0.000001594429,0.9948526,0.0001507806,0.004622451,0.0002903895,0.000004018369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1130505,0.001170859,0.8585041,0.001011869,0.0001992131,0.0001225885,0.0003569572,0.0004067261,0.02517726],"genre_scores_gemma":[0.7809914,0.001025079,0.2025869,0.0004313786,0.0001548014,0.0005164763,0.0006406641,0.0002199389,0.01343333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007482386,"threshold_uncertainty_score":0.01487768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01336204837460822,"score_gpt":0.2533212002946023,"score_spread":0.239959151919994,"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."}}