{"id":"W4415702568","doi":"10.1016/j.cma.2025.118502","title":"Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials","year":2025,"lang":"en","type":"article","venue":"Computer Methods in Applied Mechanics and Engineering","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Argonne National Laboratory; Basic Energy Sciences; Office of Naval Research; Northwestern University; Sandia National Laboratories; Laboratory Directed Research and Development; U.S. Department of Energy; National Nuclear Security Administration; Office of Science; National Science Foundation","keywords":"Benchmark (surveying); Artificial neural network; Multiscale modeling; Transformative learning; Domain (mathematical analysis); Work (physics); Ab initio; Deep learning; Potential field","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001155482,0.000620041,0.00137975,0.0007892384,0.0008760698,0.001296584,0.002431275,0.00240789,0.003687615],"category_scores_gemma":[0.004494389,0.0006032157,0.0007035328,0.0008138437,0.001754561,0.002216778,0.001755857,0.002540132,0.0006132909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001155906,"about_ca_system_score_gemma":0.001230914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003507988,"about_ca_topic_score_gemma":0.005584335,"domain_scores_codex":[0.9995669,0.0002010913,0.00001966822,0.0000298936,0.0001392924,0.00004320406],"domain_scores_gemma":[0.9986109,0.0008160615,0.00007525554,0.0002142137,0.0001822646,0.0001013506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004378704,0.00006733746,0.0001950207,0.0001086437,0.00002723727,0.00008740249,0.0000602139,0.6857999,0.001833693,0.3021562,0.001602167,0.008018366],"study_design_scores_gemma":[0.00000509526,0.000002532535,0.0000213281,0.000003423756,9.727775e-7,0.000002633693,0.000003486007,0.9669507,0.00007262756,0.03279098,0.0001429938,0.000003238012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09384797,0.001433603,0.8851206,0.001974155,0.0005372012,0.0001535035,0.0002558783,0.000862986,0.01581413],"genre_scores_gemma":[0.8166658,0.000925527,0.1681631,0.0009344353,0.0003952206,0.0004908394,0.0003856936,0.0006273335,0.01141192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003687615,"threshold_uncertainty_score":0.01233631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01699517140551311,"score_gpt":0.3112421537914737,"score_spread":0.2942469823859606,"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."}}