{"id":"W4402650770","doi":"10.1038/s41524-024-01413-4","title":"MEPO-ML: a robust graph attention network model for rapid generation of partial atomic charges in metal-organic frameworks","year":2024,"lang":"en","type":"article","venue":"npj Computational Materials","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Total; Mitacs; Alliance de recherche numérique du Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; University of Ottawa","keywords":"Metal-organic framework; Partial charge; Graph; Computer science; Theoretical computer science; Chemistry; Charge (physics); Physics; Quantum mechanics; Physical chemistry","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":[0.0004202668,0.0006565343,0.0006381721,0.0004723267,0.0003395024,0.0004677066,0.001805154,0.001166283,0.003216409],"category_scores_gemma":[0.001612035,0.0003566354,0.0006917554,0.0003163184,0.000346268,0.0008672146,0.0006438632,0.0009442605,0.0003612522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001121576,"about_ca_system_score_gemma":0.0009526378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01661777,"about_ca_topic_score_gemma":0.01326267,"domain_scores_codex":[0.999904,0.00002404227,0.000003933069,0.00002262667,0.00002393322,0.00002143926],"domain_scores_gemma":[0.9996315,0.0002348333,0.00002888845,0.00001667513,0.00005976322,0.00002825716],"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":[0.00001837906,0.0000147541,0.0002066781,0.00001579343,0.000008723109,0.00002403704,0.000005580059,0.993526,0.0003508114,0.001076512,0.000480709,0.004272036],"study_design_scores_gemma":[0.000001292098,0.000002075344,0.00001361587,6.058262e-7,5.066784e-7,9.746441e-7,4.54139e-7,0.9996402,0.00005032842,0.0002573864,0.00003198252,6.141904e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3113197,0.001360143,0.6677612,0.001496613,0.0001860013,0.0001780846,0.001672301,0.005730214,0.01029573],"genre_scores_gemma":[0.9434066,0.0002440715,0.05101211,0.000307472,0.00004745845,0.0002200679,0.0009400959,0.0002228002,0.003599329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01661777,"threshold_uncertainty_score":0.03304207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03396385085223951,"score_gpt":0.2839576733863097,"score_spread":0.2499938225340702,"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."}}