{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003205173,0.000564753,0.0005831844,0.00006514049,0.0002053745,0.0002868289,0.00124047,0.0006965714,0.0001295117],"category_scores_gemma":[0.000466861,0.0004119303,0.0004123345,0.0002811889,0.00009667251,0.000032537,0.001211987,0.0007590896,0.00001643052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000977448,"about_ca_system_score_gemma":0.0002077935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001774143,"about_ca_topic_score_gemma":0.0000698546,"domain_scores_codex":[0.99764,0.00002486255,0.0003929283,0.0009654225,0.0003863297,0.0005904408],"domain_scores_gemma":[0.9980648,0.0004092347,0.0003104068,0.0008324487,0.0001856307,0.0001974507],"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":[0.0004470038,0.0001809395,0.0005640763,0.01790755,0.001700594,0.00005464292,0.002376533,0.007192411,0.9454365,0.0006133812,0.01293266,0.01059367],"study_design_scores_gemma":[0.0005307035,0.00001311679,0.00005536076,0.0005507584,0.0003213631,0.000009387575,0.0006664623,0.02647567,0.938781,0.02073736,0.01097818,0.000880629],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8058122,0.002992379,0.1746847,0.009037207,0.001304395,0.001796314,0.0005877575,0.001106528,0.00267845],"genre_scores_gemma":[0.9854367,0.0001746409,0.01043165,0.0003839581,0.0005279135,0.0005097712,0.000538767,0.0002090422,0.001787516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1796245,"threshold_uncertainty_score":0.9998332,"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."}}