{"id":"W4285497405","doi":"10.1149/ma2022-013473mtgabs","title":"Towards Bottom-up Design of Porous Electrode Microstructures – an Approach Coupling Evolutionary Algorithms and Pore Network Modeling","year":2022,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Advanced battery technologies research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiphysics; Electrode; Nanotechnology; Computer science; Porosity; Materials science; Process engineering; Electrolyte; Microstructure; Mechanical engineering; Engineering; Finite element method; Composite material; Chemistry","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"],"consensus_categories":[],"category_scores_codex":[0.0006903713,0.0002339194,0.0002755366,0.0001512714,0.0003492725,0.00003378923,0.000383228,0.0001245351,0.000007383517],"category_scores_gemma":[0.0001104024,0.0002648829,0.00003365274,0.000323107,0.00007410887,0.0001617288,0.0002357552,0.0009174044,4.337204e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001895287,"about_ca_system_score_gemma":0.00006567451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005563658,"about_ca_topic_score_gemma":6.940554e-7,"domain_scores_codex":[0.9981079,0.00004198854,0.0004050194,0.0003777231,0.0004146335,0.0006527958],"domain_scores_gemma":[0.9993301,0.00009599694,0.000095557,0.0003287815,0.00006626588,0.00008330563],"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":[0.00002889006,0.00001903608,0.0001134487,0.0000581889,0.00002965733,0.00001826757,0.0001299812,0.9446693,0.05309992,0.000008330092,0.0001486596,0.001676323],"study_design_scores_gemma":[0.0002092391,0.00008494106,0.0003849826,0.00002277997,0.00001107322,0.0001282054,0.0004763041,0.9829448,0.01367378,0.001766902,0.00003780827,0.0002592019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502608,0.003817842,0.04356525,0.00003167605,0.0002461436,0.0003624106,0.00002638926,0.0007576389,0.0009318987],"genre_scores_gemma":[0.8433871,0.00009428837,0.1562336,0.000009746437,0.0001007774,0.00004297845,0.00005490324,0.0000652572,0.00001134385],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1126684,"threshold_uncertainty_score":0.9999803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0232167157069305,"score_gpt":0.2529832180826431,"score_spread":0.2297665023757126,"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."}}