{"id":"W4319773267","doi":"10.26434/chemrxiv-2023-hswx6","title":"A local Gaussian Processes method for fittingpotential surfaces that obviates the need to invertlarge matrices","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Chemical Thermodynamics and Molecular Structure","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Reims Champagne-Ardenne","keywords":"Interpolation (computer graphics); Potential energy; Gaussian process; Gaussian; Kriging; Potential energy surface; Mathematics; Superposition principle; Matrix (chemical analysis); Determinantal point process; Random matrix; Applied mathematics; Mathematical analysis; Computer science; Ab initio; Physics; Quantum mechanics; Statistics","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":[0.001223883,0.0009096135,0.0007466609,0.001203222,0.0007890885,0.0009159305,0.001919652,0.001079251,0.01256139],"category_scores_gemma":[0.002578201,0.0005547598,0.001183241,0.001458689,0.0007119488,0.001052642,0.002018944,0.002676014,0.004581691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004643007,"about_ca_system_score_gemma":0.001389311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003532229,"about_ca_topic_score_gemma":0.007019709,"domain_scores_codex":[0.9993624,0.0001541252,0.00002115068,0.00009056702,0.0003221053,0.00004961092],"domain_scores_gemma":[0.9993692,0.0002280699,0.00003790147,0.0001860324,0.000141116,0.00003766998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000203791,0.0003187677,0.001712149,0.0004551116,0.0002487369,0.0003418369,0.0004870595,0.2208547,0.0393194,0.2207136,0.02232237,0.4930225],"study_design_scores_gemma":[0.00004496367,0.00006509403,0.000332855,0.0000205205,0.00002594304,0.0001764618,0.00005238744,0.9349455,0.009591007,0.03343979,0.02126589,0.00003958807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001459059,0.0000330173,0.9957582,0.00003399794,0.00002131094,0.00002763868,0.00006873394,0.001557176,0.001040744],"genre_scores_gemma":[0.04596225,0.0001225466,0.9436686,0.0001244232,0.0000380114,0.0002676979,0.0004846955,0.002270782,0.007060979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01256139,"threshold_uncertainty_score":0.04202199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02269830936051077,"score_gpt":0.2865508874337397,"score_spread":0.263852578073229,"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."}}