{"id":"W4380686968","doi":"10.25418/crick.24297949.v1","title":"14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon","year":2023,"lang":"en","type":"preprint","venue":"PubMed","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"European Social Fund; High Energy Physics; Basic Energy Sciences; Francis Crick Institute; National Institute of General Medical Sciences; Division of Chemical, Bioengineering, Environmental, and Transport Systems; Grantham Foundation for the Protection of the Environment; Engineering and Physical Sciences Research Council; National Center for Advancing Translational Sciences; Consejo Superior de Investigaciones Científicas; Medical Research Council; Office of Science; National Institute of Standards and Technology; U.S. Department of Energy; European Commission; Agencia Estatal de Investigación; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Institutes of Health; Wellcome Trust; Cancer Research UK; National Science Foundation; U.S. Department of Commerce; Center for Hierarchical Materials Design; Nvidia","keywords":"Reflection (computer programming); Nanotechnology; Computer science; Engineering ethics; Data science; Chemistry; Engineering; Materials 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":[],"consensus_categories":[],"category_scores_codex":[0.0005607429,0.0001602575,0.0002048467,0.0001319243,0.0000311948,0.000064197,0.0001550461,0.0001795073,0.000003171188],"category_scores_gemma":[0.0001114375,0.0001529002,0.00002486365,0.0002166831,0.00006716223,0.00004208281,0.00005690784,0.0001959249,6.867947e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001671363,"about_ca_system_score_gemma":0.00005087761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005612471,"about_ca_topic_score_gemma":0.00002364753,"domain_scores_codex":[0.9989918,0.000004855194,0.000148601,0.0002592693,0.0002753297,0.0003201694],"domain_scores_gemma":[0.9995736,0.00001472903,0.00003275441,0.0002188169,0.00004087464,0.0001191732],"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":[0.0000543377,0.0001702963,0.00001862491,0.01960583,0.0001764332,0.000007594985,0.009503418,0.02206014,0.8000742,0.0002770913,0.004424385,0.1436277],"study_design_scores_gemma":[0.0004852605,0.00001330374,0.008790469,0.0003575415,0.00006452986,0.000006134067,0.000261525,0.06603847,0.9212842,0.001009232,0.001040349,0.0006489736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943268,0.0001362848,0.001949881,0.000760089,0.001021047,0.0004502773,0.0003344613,0.000457957,0.0005632298],"genre_scores_gemma":[0.9981118,0.0001646455,0.000240223,0.000009150411,0.0001383004,0.0008099808,0.00008394093,0.00003527832,0.000406699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1429787,"threshold_uncertainty_score":0.6235091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03471325977683185,"score_gpt":0.2372250916971063,"score_spread":0.2025118319202745,"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."}}