{"id":"W4414999823","doi":"10.1016/j.actamat.2025.121619","title":"Microstructure-agnostic deep learning for mechanistic discovery of corrosion-resistant Co-Cr-Fe-Ni MPEAs","year":2025,"lang":"en","type":"article","venue":"Acta Materialia","topic":"Hydrogen embrittlement and corrosion behaviors in metals","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Oak Ridge National Laboratory; Air Force Office of Scientific Research; Directorate for Mathematical and Physical Sciences; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Corrosion; Passivation; Deep learning; Ranking (information retrieval); Density functional theory; Key (lock); Work (physics); Surface (topology)","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005931171,0.0003004169,0.0005403716,0.0001355371,0.0003273108,0.0003338932,0.0005476984,0.0001464747,0.001184017],"category_scores_gemma":[0.0004101132,0.0002672862,0.0001403837,0.000171683,0.0001481578,0.000257669,0.0001972314,0.00008860061,0.00005363267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006641852,"about_ca_system_score_gemma":0.00008455885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007108021,"about_ca_topic_score_gemma":0.00001476547,"domain_scores_codex":[0.99782,0.0001679082,0.0007192472,0.0005048545,0.000319699,0.0004682762],"domain_scores_gemma":[0.998871,0.0001389073,0.0003446274,0.0004506356,0.0001285294,0.00006635925],"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":[0.0004079861,0.00006528768,0.0003127215,0.0002297413,0.00000680619,0.000004653006,0.0000797276,0.000007377771,0.9939336,0.00307165,0.001786039,0.00009439423],"study_design_scores_gemma":[0.000772558,0.0001652576,0.0005485848,0.0001854,0.0001898703,0.000003673397,0.0001143201,0.00006247841,0.990314,0.002080869,0.005281162,0.0002818393],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834936,0.0001032599,0.007763074,0.00005161183,0.007361043,0.0006555843,0.000328791,0.00008947971,0.0001536088],"genre_scores_gemma":[0.9940823,0.00002640592,0.001444401,0.0001015463,0.00009843715,0.0001182304,0.0001802564,0.00003722054,0.003911242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01058871,"threshold_uncertainty_score":0.9999779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.012137791954874,"score_gpt":0.2824630498429888,"score_spread":0.2703252578881148,"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."}}