{"id":"W4402507667","doi":"10.1039/d4fd00153b","title":"Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science","year":2024,"lang":"en","type":"article","venue":"Faraday Discussions","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Structural Genomics Consortium; Vector Institute; University of Toronto","funders":"Canada First Research Excellence Fund; King Abdullah University of Science and Technology; University of Toronto","keywords":"Perspective (graphical); Field (mathematics); Computer science; Diversity (politics); Maturity (psychological); Artificial intelligence; Chemistry; Data science; Sociology; Psychology; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009662574,0.001121159,0.0007245858,0.001356433,0.00311554,0.006569285,0.001376799,0.005159273,0.06597633],"category_scores_gemma":[0.01289675,0.0004012675,0.0008177173,0.0007243783,0.002897039,0.009155229,0.003974797,0.00992065,0.03652441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003166927,"about_ca_system_score_gemma":0.004708783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001709068,"about_ca_topic_score_gemma":0.003449648,"domain_scores_codex":[0.996291,0.001137497,0.0001505009,0.0004774219,0.001443784,0.0004998872],"domain_scores_gemma":[0.9904885,0.002635826,0.0002952661,0.0004056336,0.002399319,0.003775412],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002872069,0.00002915172,0.000086491,0.00006662944,0.000009289104,0.0000341959,0.00009477643,0.0001272419,0.0002539183,0.03188759,0.9409101,0.02647183],"study_design_scores_gemma":[0.000007380421,0.00002588007,0.000134303,0.0001208375,0.000008317783,0.00005385738,0.0001238053,0.0001772744,0.0002766286,0.02518772,0.9738689,0.00001508293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.0006684468,0.03689997,0.006914181,0.7280006,0.1392203,0.0000387936,0.0003108486,0.0002904959,0.0876564],"genre_scores_gemma":[0.03137536,0.06285932,0.01337599,0.118173,0.1351276,0.000145087,0.0004927125,0.0008684767,0.6375825],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06597633,"threshold_uncertainty_score":0.2207128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04662916291121966,"score_gpt":0.3886794174781502,"score_spread":0.3420502545669305,"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."}}