{"id":"W3014012254","doi":"10.1021/acs.jctc.9b01267","title":"Benchmarking Quasiclassical Mapping Hamiltonian Methods for Simulating Electronically Nonadiabatic Molecular Dynamics","year":2020,"lang":"en","type":"article","venue":"Journal of Chemical Theory and Computation","topic":"Spectroscopy and Quantum Chemical Studies","field":"Physics and Astronomy","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Eidgenössische Technische Hochschule Zürich; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Semiclassical physics; Hamiltonian (control theory); Statistical physics; Operator (biology); Benchmark (surveying); Physics; Quantum; Hamiltonian mechanics; Quantum mechanics; Phase space; Mathematics; Chemistry; Mathematical optimization","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":[0.0004282985,0.0001218567,0.000298032,0.00002185239,0.00007251708,0.00003930807,0.00008448533,0.00004096052,0.00001123626],"category_scores_gemma":[0.0001583518,0.000106349,0.0001561964,0.0001021537,0.00005875575,0.00007932405,0.00003537122,0.0002543707,2.558623e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004756199,"about_ca_system_score_gemma":0.00004459855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.467706e-7,"about_ca_topic_score_gemma":1.985466e-8,"domain_scores_codex":[0.9990752,0.0001097486,0.0003940008,0.0001408846,0.00009473402,0.000185477],"domain_scores_gemma":[0.998274,0.00121992,0.0002575983,0.00003128488,0.00009203792,0.0001251319],"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":[0.0004045867,0.00009069075,0.0003950274,0.00007670547,0.0003272422,0.000002218514,0.0009269834,0.0015353,0.4885726,0.2644863,0.00002104636,0.2431613],"study_design_scores_gemma":[0.0009224074,0.0002613371,0.00003921943,0.0000673514,0.0001028885,0.0000043241,0.0003237327,0.4403373,0.101797,0.4557615,0.0001883887,0.0001945311],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4008124,0.00009025575,0.5984154,0.0004309233,0.00002618933,0.00005472038,0.000001671807,0.000005406433,0.000163052],"genre_scores_gemma":[0.9177086,0.000001253028,0.08169387,0.0002258925,0.000347218,0.000002848879,0.000008476834,0.00001055389,0.000001258985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5168962,"threshold_uncertainty_score":0.4336787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01563814440083529,"score_gpt":0.3310240130577664,"score_spread":0.3153858686569311,"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."}}