{"id":"W4389115740","doi":"10.48550/arxiv.2311.15170","title":"A unified moment tensor potential for silicon, oxygen, and silica","year":2023,"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":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Nuclear Waste Management Organization","keywords":"Interatomic potential; Moment (physics); Tensor (intrinsic definition); Silicon; Ab initio; Moment tensor; Density functional theory; Point (geometry); Materials science; Range (aeronautics); Charge (physics); Fidelity; Computer science; Chemical physics; Statistical physics; Physics; Condensed matter physics; Molecular dynamics; Chemistry; Computational chemistry; Quantum mechanics; Mathematics; Optoelectronics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009863536,0.0004167933,0.0005112835,0.0002487223,0.0003630168,0.0002826679,0.001074887,0.0003176826,0.00028148],"category_scores_gemma":[0.0002255772,0.0004435138,0.0001607802,0.0002693932,0.0003708129,0.0001747976,0.001778001,0.000313269,0.000291552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001421803,"about_ca_system_score_gemma":0.0001637249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003661803,"about_ca_topic_score_gemma":0.00004194105,"domain_scores_codex":[0.9969807,0.0002224426,0.000342164,0.001662257,0.0001659422,0.0006264427],"domain_scores_gemma":[0.998091,0.0001928129,0.0003711262,0.0008971293,0.00020375,0.0002441843],"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":[0.0003726229,0.0001090709,0.0007815129,0.0005918131,0.00005444251,0.0002000845,0.0003267424,0.6824958,0.2927373,0.02061074,0.001619198,0.000100574],"study_design_scores_gemma":[0.002224426,0.0004154205,0.01240326,0.000220987,0.000428643,0.00002442052,0.0004422092,0.9056188,0.01168886,0.06092793,0.003701835,0.001903222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9471616,0.00003013844,0.04893572,0.0004515886,0.001647058,0.0008884764,0.000230239,0.0004888796,0.0001663501],"genre_scores_gemma":[0.9920485,0.00007926419,0.00173895,0.0001054045,0.0002035607,0.00001246383,0.00004443784,0.00005631808,0.005711089],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2810485,"threshold_uncertainty_score":0.9998016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06593923935269759,"score_gpt":0.2138009758522832,"score_spread":0.1478617364995856,"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."}}