{"id":"W2091221599","doi":"10.1021/ct4000923","title":"Hirshfeld-E Partitioning: AIM Charges with an Improved Trade-off between Robustness and Accurate Electrostatics","year":2013,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Advanced Chemical Physics Studies","field":"Physics and Astronomy","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Electrostatics; Generalization; Computation; Robustness (evolution); Scheme (mathematics); Computer science; Molecule; Transferability; Force field (fiction); Ab initio; Atomic charge; Chemical physics; Field (mathematics); Charge (physics); Computational chemistry; Chemistry; Algorithm; Physics; Mathematics; Quantum mechanics; Physical chemistry; Artificial intelligence","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":[0.0007529875,0.000488531,0.0004844435,0.0005729047,0.0005511175,0.0006979433,0.001808089,0.000956776,0.003213317],"category_scores_gemma":[0.002830766,0.0001940382,0.0004399964,0.0005288964,0.0005192996,0.001628426,0.001240321,0.0006936501,0.000752332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005881066,"about_ca_system_score_gemma":0.0009277743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003076503,"about_ca_topic_score_gemma":0.004235948,"domain_scores_codex":[0.9997037,0.00009723551,0.00002086304,0.00003385676,0.0001029908,0.00004134122],"domain_scores_gemma":[0.9992381,0.0002303776,0.00005654871,0.0002824482,0.0001545072,0.00003801863],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008750458,0.0003629893,0.002998832,0.0002178373,0.000122646,0.0002644033,0.0005012679,0.483942,0.04919802,0.10248,0.005295886,0.353741],"study_design_scores_gemma":[0.0001200081,0.0001711363,0.0004958886,0.00001255071,0.00001278433,0.00008009226,0.00004710061,0.9639851,0.01310603,0.01783372,0.004107221,0.0000283652],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1338962,0.0003217509,0.8551203,0.0002675807,0.00007915759,0.0001843278,0.0001588544,0.001625545,0.008346265],"genre_scores_gemma":[0.4741375,0.0001290584,0.5193444,0.0001997533,0.00003070506,0.000183707,0.0002674663,0.0003383313,0.005369069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003213317,"threshold_uncertainty_score":0.01074964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009954572540567018,"score_gpt":0.2573035347571601,"score_spread":0.2473489622165931,"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."}}