{"id":"W4285042298","doi":"10.5281/zenodo.6823150","title":"Supplementary Information of Geometry Relaxation and Transition State Search with Quantum Machine Learning","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto","funders":"Horizon 2020 Framework Programme","keywords":"Transition (genetics); Information geometry; Relaxation (psychology); Geometry; State (computer science); Quantum; Statistical physics; Physics; Computer science; Theoretical physics; Artificial intelligence; Quantum mechanics; Psychology; Mathematics; Algorithm; Chemistry; Neuroscience","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002256877,0.000104014,0.0001283081,0.0003161538,0.002341991,0.0003828884,0.0004641286,0.00001781024,0.0290872],"category_scores_gemma":[0.0001763075,0.0001027121,0.0000164681,0.0005380442,0.0001731526,0.0005859537,0.0007082993,0.0002759763,0.000220141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001059071,"about_ca_system_score_gemma":0.000006637886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002810886,"about_ca_topic_score_gemma":6.910422e-7,"domain_scores_codex":[0.9977399,0.0007623072,0.0002975911,0.0002513075,0.0006859674,0.0002630003],"domain_scores_gemma":[0.9992328,0.00003240174,0.0002044423,0.000197999,0.0002520757,0.00008028335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001429703,0.0002768134,0.0003373024,0.0006179194,0.00003840654,0.00001715013,0.02948504,0.1766273,0.7388788,0.004480741,0.005421185,0.04238963],"study_design_scores_gemma":[0.00377949,0.005918173,0.008277313,0.0001113385,0.00005224785,0.0007083467,0.008530791,0.1612342,0.04868617,0.0005590929,0.7612484,0.000894452],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823037,0.00001670561,0.01452724,0.0005093098,0.00005645302,0.0003533205,0.0009293422,0.0002212035,0.001082663],"genre_scores_gemma":[0.9956939,0.0000183943,0.001084873,0.00006785419,0.00001583832,1.583015e-7,0.002869784,0.000194914,0.00005432728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7558272,"threshold_uncertainty_score":0.9989568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289570850154723,"score_gpt":0.2207444624413465,"score_spread":0.2078487539397993,"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."}}