{"id":"W4400201634","doi":"10.26434/chemrxiv-2024-bfv3d","title":"A Foundational Model for Reaction Networks on Metal Surfaces","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; McMaster University; University of Toronto","funders":"Horizon 2020; Office of Science; European Commission; NCCR Catalysis; Erasmus+; University of Minnesota; U.S. Department of Energy; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Generalitat de Catalunya; National Science Foundation","keywords":"Catalysis; Chemistry; Computer science; Biochemical engineering; Organic chemistry; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.001545081,0.0003485988,0.0003799117,0.0001065312,0.0001833453,0.0005580701,0.0006171736,0.0002986121,0.0002877538],"category_scores_gemma":[0.0002845325,0.0003080931,0.0001777413,0.00009942178,0.0001415278,0.0001012065,0.0006896409,0.0005182087,0.0004073788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001585544,"about_ca_system_score_gemma":0.0002366335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000397794,"about_ca_topic_score_gemma":0.000009567431,"domain_scores_codex":[0.997646,0.00007077637,0.0004107999,0.00103253,0.0004480524,0.0003917951],"domain_scores_gemma":[0.9987488,0.0002035857,0.0002820287,0.000549961,0.0001284928,0.00008709426],"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":[0.00005094659,0.00003058006,0.000005319652,0.0002157036,0.00001209374,0.000001293951,0.00009947611,0.6694978,0.3245251,0.003618319,0.001816782,0.0001266228],"study_design_scores_gemma":[0.0001040183,0.0000320994,0.00005401956,0.0001497836,0.00005394496,0.000003211323,0.000005297939,0.9343464,0.03052005,0.03378105,0.0006300229,0.0003200459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.931673,0.000156538,0.0567628,0.0009288474,0.007139273,0.0007261409,0.00005761086,0.0004979136,0.002057891],"genre_scores_gemma":[0.9750179,0.00001618172,0.02021246,0.0001669317,0.0008773264,0.0003444098,0.0001864678,0.00006439569,0.0031139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.294005,"threshold_uncertainty_score":0.9999371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03956188224065012,"score_gpt":0.3129540253505044,"score_spread":0.2733921431098543,"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."}}