{"id":"W4399076323","doi":"10.1021/acs.jcim.4c00421","title":"Learning Electronic Polarizations in Aqueous Systems","year":2024,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University; Deutsche Forschungsgemeinschaft; Queen's University Belfast","keywords":"Wannier function; Polarization (electrochemistry); Computer science; Dipole; Computation; Ab initio; Point particle; Dielectric; Algorithm; Statistical physics; Computational physics; Physics; Chemistry; Quantum mechanics; Physical chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.001039082,0.00006086699,0.0001206632,0.0001734065,0.00003697591,0.0004077128,0.000102234,0.00004692072,0.00002995648],"category_scores_gemma":[0.0002388938,0.00004869499,0.00002237521,0.0001351671,0.00001915083,0.001269034,0.00002961046,0.0002904343,0.00001793498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006921592,"about_ca_system_score_gemma":0.0001049135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002945148,"about_ca_topic_score_gemma":1.935732e-7,"domain_scores_codex":[0.9990277,0.00003860758,0.0005184347,0.00005397867,0.0002053854,0.0001558464],"domain_scores_gemma":[0.9996637,0.00004572515,0.0001213206,0.00003697062,0.0000829539,0.00004930221],"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":[0.00001149596,0.000004274223,0.00002162441,0.00008388323,0.00000188152,0.00000200298,0.001500943,0.6617474,0.3333722,0.002166071,0.00001724225,0.001071019],"study_design_scores_gemma":[0.00009831149,0.00002816792,0.000001368623,0.000139911,0.000004434542,0.0001468387,0.0002078084,0.9932343,0.004644849,0.0003768192,0.001060506,0.00005672706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8795626,0.0005378535,0.1191362,0.0002091786,0.000269852,0.00003764851,6.998146e-7,0.00003029226,0.0002157012],"genre_scores_gemma":[0.9986762,0.00007554717,0.001104098,0.00005494633,0.00007177157,0.00000120139,0.000001961411,0.000003796102,0.00001043999],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3314869,"threshold_uncertainty_score":0.3931584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008642448853044958,"score_gpt":0.2570594471264121,"score_spread":0.2484169982733671,"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."}}