{"id":"W4391840074","doi":"10.1002/jcc.27322","title":"Reinforcement learning for in silico determination of adsorbate—substrate structures","year":2024,"lang":"en","type":"article","venue":"Journal of Computational Chemistry","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Calgary","funders":"Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Reinforcement learning; Computer science; Chemistry; Artificial intelligence; Artificial neural network","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":[0.0008777633,0.00008595606,0.0001731352,0.00008171311,0.00003431823,0.00008190153,0.0001941805,0.00004467949,0.0003343424],"category_scores_gemma":[0.0002819746,0.00007476414,0.00006445619,0.0001183317,0.00005890838,0.0001961311,0.00002438805,0.0001450288,0.000002926169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006456484,"about_ca_system_score_gemma":0.0001860504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002621668,"about_ca_topic_score_gemma":2.65147e-7,"domain_scores_codex":[0.9987515,0.00003632874,0.0005909738,0.0001230197,0.0003784995,0.0001196626],"domain_scores_gemma":[0.9990293,0.0002937478,0.0003805012,0.00004723868,0.0002121195,0.00003709786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002896703,0.000006070503,0.0001031278,0.0001784671,0.000001936737,0.00000502826,0.0001345449,0.5289423,0.469984,0.00007288572,0.000009945274,0.0005326864],"study_design_scores_gemma":[0.0003628222,0.0001078137,0.001681013,0.0002491157,0.00001268728,0.0001101456,0.00006564557,0.2481719,0.7203499,0.02856181,0.0002176504,0.00010943],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.958951,0.0001154564,0.04027537,0.00009192686,0.0002673202,0.00005502844,0.00000297841,0.000009502369,0.0002313942],"genre_scores_gemma":[0.98142,0.000003229675,0.01832255,0.000008873063,0.0001359661,0.000002395488,0.000006956522,0.000007738504,0.00009222094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2807704,"threshold_uncertainty_score":0.3660814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01113924811510195,"score_gpt":0.2928391596709993,"score_spread":0.2816999115558973,"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."}}