{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001080553,0.0001201344,0.0002239835,0.00001930371,0.00006773281,0.0000551351,0.00005095562,0.00002528624,0.000008776827],"category_scores_gemma":[0.000009614479,0.00008978957,0.00002933501,0.00005852437,0.0001296237,0.0004257349,0.00001859023,0.0001912628,2.968007e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001290322,"about_ca_system_score_gemma":0.00001328884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.035353e-7,"about_ca_topic_score_gemma":1.817516e-8,"domain_scores_codex":[0.9993783,0.00004481295,0.0002356253,0.0001099208,0.00009079486,0.0001405019],"domain_scores_gemma":[0.9992379,0.0002626659,0.0002519876,0.00004145881,0.0001016836,0.0001042923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001224269,0.0007894665,0.01855069,0.0001898713,0.001297272,0.000004648485,0.003962648,0.007890589,0.5150755,0.08257627,0.000199569,0.3682392],"study_design_scores_gemma":[0.001193419,0.0003819014,0.00162119,0.00006062392,0.0001202594,0.000008402152,0.0007940948,0.005759799,0.1354665,0.8543211,0.00002054729,0.000252272],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8549456,0.00006542905,0.144688,0.0001657268,0.00001097319,0.00006662397,0.000003259721,0.000006991764,0.00004740289],"genre_scores_gemma":[0.9971516,0.000006180062,0.002516886,0.00003079278,0.0002605739,0.000004997594,0.00001416447,0.00001100863,0.000003843015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7717448,"threshold_uncertainty_score":0.3661512,"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."}}