{"id":"W4376124302","doi":"10.5281/zenodo.7922012","title":"Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"","keywords":"Legendre polynomials; Associated Legendre polynomials; Legendre function; Charge (physics); Applied mathematics; Computer science; Mathematics; Materials science; Physics; Mathematical analysis; Quantum mechanics; Orthogonal polynomials; Classical orthogonal polynomials","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007778714,0.00149923,0.001396285,0.001450625,0.0009089477,0.001099927,0.003558896,0.001713371,0.05166122],"category_scores_gemma":[0.002249573,0.0004912578,0.00110213,0.003071949,0.0002706788,0.001062109,0.0007997017,0.00238362,0.02921138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001199436,"about_ca_system_score_gemma":0.001393571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005889952,"about_ca_topic_score_gemma":0.01133157,"domain_scores_codex":[0.9991679,0.0001334843,0.00003897081,0.0001205096,0.0004192083,0.0001198343],"domain_scores_gemma":[0.9988686,0.0003533198,0.00007349981,0.0003331928,0.0002984332,0.0000729341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003281558,0.0002425558,0.002257186,0.00110503,0.0001256459,0.0001714606,0.0000421757,0.03831537,0.002837863,0.01010183,0.9228077,0.0216651],"study_design_scores_gemma":[0.0009331695,0.0003623348,0.01079079,0.0004055097,0.0001028047,0.0003766007,0.0001275947,0.3554398,0.02513593,0.03951028,0.5666171,0.0001979431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.03606147,0.001101283,0.01695457,0.0005835796,0.0004055056,0.0001476028,0.8969988,0.02003212,0.02771505],"genre_scores_gemma":[0.05036756,0.0004202416,0.01591522,0.0003179473,0.00009727439,0.0004053604,0.9230041,0.003882126,0.005590137],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05166122,"threshold_uncertainty_score":0.172824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02944270806459546,"score_gpt":0.2645193256469033,"score_spread":0.2350766175823079,"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."}}