{"id":"W4401702797","doi":"10.1063/5.0216781","title":"<tt>CuGBasis</tt>: High-performance CUDA/Python library for efficient computation of quantum chemistry density-based descriptors for larger systems","year":2024,"lang":"en","type":"article","venue":"The Journal of Chemical Physics","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University; Compute Canada","keywords":"Python (programming language); CUDA; Computer science; Computation; Computational science; Parallel computing; Operating system; Programming language","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":[],"consensus_categories":[],"category_scores_codex":[0.001412061,0.0001773514,0.0003810183,0.0000329404,0.00009872688,0.0001542121,0.0005209264,0.00007463385,0.00001631194],"category_scores_gemma":[0.0001564619,0.0001176764,0.0001384133,0.000211458,0.0001916296,0.0002599909,0.00006580176,0.0001920392,0.000006184622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005835997,"about_ca_system_score_gemma":0.0001893218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000457476,"about_ca_topic_score_gemma":7.552123e-9,"domain_scores_codex":[0.9983383,0.00009245654,0.0006231166,0.0001895068,0.0004801848,0.0002763837],"domain_scores_gemma":[0.9983077,0.0006547225,0.0005343499,0.0001956364,0.000230616,0.00007698351],"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":[0.0002511329,0.00006538659,0.00002038111,0.001184869,0.0000111624,6.978229e-7,0.0001389066,0.1407835,0.8558845,0.0003751833,0.001163617,0.0001207088],"study_design_scores_gemma":[0.0002363304,0.00007334156,0.00001464479,0.0003348724,0.00005199361,0.000009429576,0.00001670896,0.3705644,0.6276563,0.0008151582,0.0001397439,0.00008709543],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8153803,0.0001523681,0.1827747,0.0002335943,0.001130746,0.0002234015,0.00004314871,0.00005100643,0.00001074128],"genre_scores_gemma":[0.9922841,0.000003700297,0.006888285,0.00005679567,0.0006866509,0.000009903511,0.00001272799,0.00003839603,0.00001940542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.229781,"threshold_uncertainty_score":0.4798706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01283048444600294,"score_gpt":0.2464753655961,"score_spread":0.2336448811500971,"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."}}