{"id":"W6988982211","doi":"","title":"Accelerating Materials Discovery with Machine Learning","year":2024,"lang":"en","type":"dissertation","venue":"Trinity's Access to Research Output (TARA) (Trinity College Dublin)","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"","keywords":"Intuition; Computation; Property (philosophy); Transformer; Pipeline (software); Question answering; Natural language","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":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.01621429,0.00180073,0.002549572,0.003814801,0.002967326,0.01882418,0.007029374,0.001006236,0.00743619],"category_scores_gemma":[0.007248377,0.001500659,0.0003346305,0.005707934,0.0005797122,0.004515603,0.003271502,0.005178843,0.0049681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009935923,"about_ca_system_score_gemma":0.00298593,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007432536,"about_ca_topic_score_gemma":0.006588209,"domain_scores_codex":[0.9796481,0.004146258,0.002482875,0.003935811,0.00629788,0.003489091],"domain_scores_gemma":[0.9906804,0.002548532,0.001095382,0.002236954,0.00226602,0.001172691],"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":[0.02341011,0.002427433,0.002735623,0.01629353,0.0007201266,0.00493177,0.009912226,0.01545154,0.8526538,0.02630336,0.04045851,0.004701966],"study_design_scores_gemma":[0.006477538,0.006145646,0.01564125,0.007904398,0.0006119262,0.0002907422,0.005806074,0.007368801,0.8214357,0.002754726,0.1168146,0.008748577],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9721842,0.0003906391,0.0001771414,0.00149001,0.004702669,0.004544403,0.002265946,0.001064026,0.01318093],"genre_scores_gemma":[0.9241046,0.0001961094,0.002442817,0.0002287258,0.00186536,0.001427046,0.002708258,0.0005042307,0.06652283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07635611,"threshold_uncertainty_score":0.9994738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029292046347342,"score_gpt":0.4021692363650076,"score_spread":0.2992400317302734,"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."}}