{"id":"W3013352749","doi":"10.1088/1361-651x/ab7150","title":"Roadmap on multiscale materials modeling","year":2020,"lang":"en","type":"article","venue":"Modelling and Simulation in Materials Science and Engineering","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":203,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"H2020 European Research Council; Lawrence Livermore National Laboratory; FP7 Ideas: European Research Council; Basic Energy Sciences; Deutsche Forschungsgemeinschaft; Cleansky; Sandia National Laboratories; National Nuclear Security Administration; U.S. Department of Energy; European Commission; Comunidad de Madrid; Korea Institute of Materials Science; Engineering and Physical Sciences Research Council; Leverhulme Trust; Seventh Framework Programme; Division of Materials Sciences and Engineering; National Science Foundation","keywords":"Computer science; Management science; Field (mathematics); Multiscale modeling; Scale (ratio); Systems engineering; Data science; Nanotechnology; Biochemical engineering; Materials science; Engineering; Physics","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":[0.001979968,0.0002410439,0.0003390003,0.0001989668,0.0002725439,0.0006587433,0.0002764027,0.00007779412,0.000108481],"category_scores_gemma":[0.0003660821,0.0002228735,0.00001164529,0.0003279849,0.0001443895,0.0006684306,0.0001570414,0.00009894618,0.00003533169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004424442,"about_ca_system_score_gemma":0.00003661154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001026888,"about_ca_topic_score_gemma":5.42188e-7,"domain_scores_codex":[0.9977476,0.00005675226,0.0004960463,0.0007074971,0.0005089826,0.0004831168],"domain_scores_gemma":[0.9993505,0.00008752653,0.00008913421,0.0001819182,0.00008628151,0.0002046609],"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":[0.00002040977,0.000004098478,0.00001818962,0.00003607431,2.907428e-7,0.000001546633,0.0004476534,0.5235976,0.4756362,0.0002013227,4.056621e-7,0.00003627544],"study_design_scores_gemma":[0.0002245733,0.00004767425,0.00009580693,0.00007845486,0.000002852863,0.000002161566,0.00003447742,0.8296801,0.1694774,0.0001112332,0.00002438694,0.0002208227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9116418,0.00003293931,0.08732323,0.0002258118,0.0004084656,0.000183555,0.00001224388,0.0001418529,0.00003011291],"genre_scores_gemma":[0.9883072,0.00002228831,0.01127956,0.0001940634,0.0001511341,0.00001461553,0.000002663507,0.00002346189,0.000005005544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3061588,"threshold_uncertainty_score":0.9088517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02873269238294067,"score_gpt":0.2535725967670546,"score_spread":0.2248399043841139,"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."}}