{"id":"W2963501253","doi":"10.48550/arxiv.1907.08717","title":"Interpolation and extrapolation of global potential energy surfaces for polyatomic systems by Gaussian processes with composite kernels","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extrapolation; Kriging; Gaussian process; Gaussian; Interpolation (computer graphics); Kernel (algebra); Potential energy; Energy (signal processing); Range (aeronautics); Mathematics; Computer science; Mathematical optimization; Applied mathematics; Algorithm; Artificial intelligence; Statistics; Physics; Materials science","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":[0.001342998,0.0007280369,0.0007014685,0.0007768651,0.0004312281,0.0005855994,0.001233513,0.0009217625,0.00155025],"category_scores_gemma":[0.002738101,0.0003998859,0.00101874,0.0008481875,0.0008975472,0.001133519,0.0009943765,0.001373131,0.000325941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007395578,"about_ca_system_score_gemma":0.000751783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003728206,"about_ca_topic_score_gemma":0.002876267,"domain_scores_codex":[0.9997003,0.00008916677,0.00001223034,0.00004584221,0.000113139,0.00003931258],"domain_scores_gemma":[0.9992725,0.0003895006,0.00006114981,0.0001429955,0.0001021207,0.00003175576],"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":[0.00003124786,0.00002311346,0.0007419352,0.00002877993,0.0000138457,0.00005744731,0.0000545213,0.970708,0.001941772,0.01736202,0.0002328967,0.008804493],"study_design_scores_gemma":[0.000001504434,0.00000333434,0.00008252836,0.00000177746,8.467057e-7,0.000004355046,0.000003976915,0.9966211,0.0003108946,0.002872574,0.00009461643,0.000002505464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1105899,0.0001656413,0.8849531,0.0001793426,0.00002358465,0.00003621353,0.00009896098,0.000502829,0.003450396],"genre_scores_gemma":[0.788911,0.0002177073,0.2080744,0.00007349131,0.00001773786,0.0001284773,0.0003170987,0.0002139361,0.002046106],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003728206,"threshold_uncertainty_score":0.00741303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767670260300219,"score_gpt":0.1882167265493725,"score_spread":0.1705400239463703,"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."}}