{"id":"W4389625705","doi":"10.1021/acs.jcim.3c01778","title":"Artificial Intelligence Agents for Materials Sciences","year":2023,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Artificial intelligence; Statement (logic); Data science; Perspective (graphical); Applications of artificial intelligence; Human–computer interaction","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":[],"consensus_categories":[],"category_scores_codex":[0.002519141,0.00007608514,0.0001628665,0.0001585047,0.0001300187,0.0003248178,0.0002462548,0.00004527481,0.0001009101],"category_scores_gemma":[0.0006798737,0.00005832128,0.00003349418,0.0001654954,0.00009464051,0.001121495,0.0000712828,0.00005600728,0.00005064574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001627457,"about_ca_system_score_gemma":0.00005503486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005178735,"about_ca_topic_score_gemma":5.778971e-8,"domain_scores_codex":[0.9986581,0.00002417629,0.0007372253,0.00007664212,0.0003108267,0.0001929819],"domain_scores_gemma":[0.9992777,0.0000884141,0.0003347402,0.00005364172,0.0001748445,0.0000706236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004996584,0.000005952973,0.0000023963,0.00005289584,0.000001610763,3.736935e-7,0.000713303,0.09779011,0.8956035,0.002835497,0.0002014315,0.00274295],"study_design_scores_gemma":[0.00004414043,0.0000432917,0.000001279014,0.00003621807,0.000004639535,0.00001710644,0.0002280981,0.5196139,0.4711412,0.008600963,0.000204461,0.00006470561],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8589661,0.000007391468,0.1398203,0.000556625,0.0004894626,0.00007350923,0.000007995031,0.00002882758,0.00004977596],"genre_scores_gemma":[0.9812154,0.0000200206,0.01834199,0.0002535857,0.0001537345,0.000003926797,0.000003890821,0.000003437046,0.000004070193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4244623,"threshold_uncertainty_score":0.3132225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1002570798011059,"score_gpt":0.3613352483137428,"score_spread":0.2610781685126369,"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."}}