{"id":"W4411056647","doi":"10.1021/jacs.5c04828","title":"Superionic Ionic Conductor Discovery via Multiscale Topological Learning","year":2025,"lang":"en","type":"article","venue":"Journal of the American Chemical Society","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; Development and Reform Commission of Shenzhen Municipality; Michigan State University Foundation; Bristol-Myers Squibb; Division of Mathematical Sciences; Soft Science Research Project of Guangdong Province; National Institutes of Health; National Science Foundation","keywords":"Chemistry; Conductor; Ionic bonding; Fast ion conductor; Chemical physics; Topology (electrical circuits); Nanotechnology; Ion; Physical chemistry; Electrolyte; Organic chemistry; Geometry","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":[0.000465272,0.0004310053,0.0004981022,0.001029643,0.000355731,0.0008954829,0.0007627401,0.0005567051,0.001225634],"category_scores_gemma":[0.001945199,0.0002317081,0.0006504953,0.0005608233,0.000624977,0.00131639,0.001012325,0.0004797484,0.00032724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005020433,"about_ca_system_score_gemma":0.0005094976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005711224,"about_ca_topic_score_gemma":0.001012487,"domain_scores_codex":[0.9998154,0.00005205755,0.000009026012,0.0000436116,0.00005878395,0.00002116087],"domain_scores_gemma":[0.9994571,0.000208542,0.0001268642,0.00007843824,0.00008542818,0.00004369019],"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":[0.0001862186,0.0001504445,0.00707872,0.0005144538,0.0001015999,0.0002656037,0.0001026519,0.7355976,0.04512865,0.07182568,0.003648646,0.1353998],"study_design_scores_gemma":[0.000008001299,0.00004074745,0.0002392384,0.000006542769,0.000009117583,0.00003162629,0.00001482885,0.9820893,0.004308126,0.01217271,0.001071922,0.000007733021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3709191,0.001178204,0.6151096,0.0007968709,0.0000606233,0.0001186024,0.0005588809,0.001909049,0.009349053],"genre_scores_gemma":[0.864506,0.0004481222,0.1324628,0.0001333898,0.00003792461,0.000104334,0.001006249,0.0001052432,0.001195939],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001225634,"threshold_uncertainty_score":0.004100084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007954929667481393,"score_gpt":0.2746493976523001,"score_spread":0.2666944679848187,"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."}}