{"id":"W4413820841","doi":"10.1088/2632-2153/ae011a","title":"32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery","year":2025,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; McGill University","funders":"Material Measurement Laboratory; Office of Advanced Cyberinfrastructure","keywords":"Scientific discovery; Automation; Nanotechnology; Data science; Engineering; Computer science; Engineering ethics; Chemistry; Materials science; Mechanical engineering; Psychology; Cognitive science","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":["sts","scholarly_communication"],"consensus_categories":["sts"],"category_scores_codex":[0.005916823,0.0002332128,0.000405029,0.001229794,0.001438216,0.001317855,0.00105729,0.0001209683,0.00007406004],"category_scores_gemma":[0.003257529,0.0002016088,0.000009453348,0.005493963,0.01000115,0.001034345,0.001201087,0.0002414631,0.000002975204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000124083,"about_ca_system_score_gemma":0.0008417603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004202504,"about_ca_topic_score_gemma":0.00003513834,"domain_scores_codex":[0.9969632,0.00008728512,0.0005243355,0.001174592,0.0006638198,0.0005867708],"domain_scores_gemma":[0.9984682,0.00009815847,0.0003086092,0.0004893927,0.0005424125,0.00009322415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007665212,0.00004000347,0.02529714,0.0001042885,0.000001440296,0.000001044595,0.0001535139,0.00003932405,0.9620167,0.008650738,0.00001152172,0.003676631],"study_design_scores_gemma":[0.000470594,0.00008210992,0.1505428,0.0001556951,0.00001649393,0.00002712411,0.0004475939,0.009646618,0.8302909,0.005444273,0.002545024,0.0003307667],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964679,0.0002866331,0.000695124,0.001418838,0.0001874852,0.0003436689,0.00002870372,0.0001868387,0.000384737],"genre_scores_gemma":[0.9970282,0.00006135142,0.00253662,0.0000444537,0.000008597827,0.00005174262,0.000007516653,0.000009086989,0.0002524147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1317258,"threshold_uncertainty_score":0.9998618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150832997095216,"score_gpt":0.2825833235286461,"score_spread":0.2710749935576939,"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."}}