{"id":"W4377227006","doi":"10.21203/rs.3.rs-2924237/v1","title":"Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Korea Environmental Industry and Technology Institute; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Generative grammar; Transition (genetics); Diffusion; Generative model; Computer science; Artificial intelligence; Chemistry; Physics; Quantum mechanics","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":[0.0008877428,0.000818837,0.001004646,0.001434326,0.0004623461,0.000831506,0.001645983,0.001289107,0.002246527],"category_scores_gemma":[0.002908672,0.0006810027,0.0013541,0.001078922,0.001390517,0.001181259,0.001253735,0.001421547,0.0003105927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156238,"about_ca_system_score_gemma":0.0007181232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006061807,"about_ca_topic_score_gemma":0.005861367,"domain_scores_codex":[0.9996681,0.0001287786,0.00001373648,0.00008497643,0.00006933934,0.0000351189],"domain_scores_gemma":[0.9975733,0.00200119,0.0001193924,0.0001169536,0.0001005795,0.00008849208],"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":[0.00002412639,0.00001621315,0.000495934,0.00004073148,0.00001683161,0.00003471312,0.00002716807,0.9821369,0.0006772407,0.007691593,0.0002511351,0.008587436],"study_design_scores_gemma":[0.000001318189,0.000001942899,0.00001529523,7.989335e-7,6.805551e-7,0.000002221884,9.975294e-7,0.9978351,0.00007208944,0.002032886,0.00003542059,0.000001168225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1076509,0.0007567439,0.8855814,0.0005535181,0.00004651192,0.00008785815,0.000483306,0.001380308,0.003459465],"genre_scores_gemma":[0.8892871,0.0002952603,0.1075326,0.0002349896,0.00004571253,0.0001714319,0.0006911179,0.000181621,0.001560117],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006061807,"threshold_uncertainty_score":0.01205301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1176515914608775,"score_gpt":0.4033767246546883,"score_spread":0.2857251331938108,"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."}}