{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004415422,0.000437537,0.000573846,0.0006334622,0.000731733,0.00107168,0.001267055,0.0002933602,0.0008081292],"category_scores_gemma":[0.001247723,0.0004051248,0.0002548069,0.0005188901,0.000408393,0.0002400717,0.0008330355,0.001015111,0.000328363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002551576,"about_ca_system_score_gemma":0.0005214103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00331504,"about_ca_topic_score_gemma":0.0001366863,"domain_scores_codex":[0.9929137,0.001677535,0.0005799005,0.001640357,0.002057583,0.001130918],"domain_scores_gemma":[0.9960319,0.001518767,0.0001828233,0.001085048,0.0009027219,0.0002787252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001677295,0.00008721121,0.0001291093,0.000795056,0.00001480481,0.00005447385,0.002017702,0.2194905,0.7756751,0.0004358705,0.0008781259,0.0002543643],"study_design_scores_gemma":[0.0007863836,0.0003821979,0.001355257,0.001496002,0.00002969364,5.46875e-7,0.000766592,0.308752,0.5960444,0.08910815,0.0005316652,0.0007471979],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8487707,0.0001438436,0.1415579,0.003478228,0.001410081,0.001875133,0.002288873,0.0004549934,0.00002020492],"genre_scores_gemma":[0.9615442,0.0001478111,0.02984405,0.0003173712,0.000532892,0.004941147,0.002360258,0.0001885904,0.0001237453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1796307,"threshold_uncertainty_score":0.9999653,"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."}}