{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001647659,0.0006944779,0.0005596213,0.0008088864,0.001288294,0.004299196,0.001503295,0.003163769,0.01648435],"category_scores_gemma":[0.003827702,0.0003104204,0.0005223388,0.0007373351,0.00219288,0.004361397,0.002709149,0.003343841,0.005790861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001371339,"about_ca_system_score_gemma":0.001815371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008542817,"about_ca_topic_score_gemma":0.00101221,"domain_scores_codex":[0.9988847,0.0004254009,0.00005912103,0.0001546908,0.0004089817,0.00006711574],"domain_scores_gemma":[0.9986489,0.0005693749,0.0001009885,0.0002563667,0.0002868992,0.0001374649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001514567,0.00003213495,0.0001366171,0.0002258678,0.00001440412,0.00006491989,0.0001474804,0.002353218,0.0007355753,0.9177203,0.02880162,0.04975269],"study_design_scores_gemma":[0.00002037788,0.00002627546,0.00009338525,0.0001923699,0.00001101767,0.0001222298,0.00009985817,0.009388808,0.0005932492,0.4669846,0.5224514,0.00001647368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.004755395,0.03666447,0.4382405,0.06070969,0.004812382,0.0005340279,0.000606342,0.001996745,0.4516805],"genre_scores_gemma":[0.2024496,0.03442731,0.5673172,0.01365724,0.003864998,0.001556485,0.001184135,0.0005025312,0.1750406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01648435,"threshold_uncertainty_score":0.05514562,"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."}}