{"id":"W4411887190","doi":"10.1038/s41467-025-60796-0","title":"Connecting metal-organic framework synthesis to applications using multimodal machine learning","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Metal-Organic Frameworks: Synthesis and Applications","field":"Chemistry","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canada First Research Excellence Fund; Concordia University","keywords":"Computer science; Robustness (evolution); Metal-organic framework; Source code; Nanotechnology; Artificial intelligence; Materials science; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004181594,0.0007901483,0.0004125827,0.0007160669,0.0003442475,0.0005212488,0.00085993,0.0008314081,0.002113546],"category_scores_gemma":[0.001499961,0.0002902054,0.0008188468,0.0004038179,0.0004198574,0.0008106955,0.0007407104,0.0007764802,0.0004642447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001125729,"about_ca_system_score_gemma":0.0006061965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008075624,"about_ca_topic_score_gemma":0.01236401,"domain_scores_codex":[0.9998268,0.00003404894,0.00000616032,0.00007127783,0.00003611366,0.00002551315],"domain_scores_gemma":[0.9996799,0.0001676273,0.00003790743,0.0000449728,0.00004594106,0.00002355187],"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":[0.0001456618,0.0001544023,0.005276816,0.0001446213,0.00007132042,0.0001126376,0.00006356426,0.8605431,0.01464495,0.00293852,0.001619005,0.1142855],"study_design_scores_gemma":[0.000002544864,0.00001888613,0.0002556389,0.000004161818,0.000004798142,0.00001019204,0.000006715127,0.9936766,0.003429102,0.00214021,0.000446977,0.000004097725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.353345,0.001280082,0.6266938,0.000883696,0.00008561502,0.0001741302,0.001257495,0.006466578,0.009813569],"genre_scores_gemma":[0.8458096,0.0003865073,0.1497698,0.0002183366,0.00002999872,0.0001263379,0.001023102,0.0001843372,0.002451929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008075624,"threshold_uncertainty_score":0.01605725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01926534151149434,"score_gpt":0.3049029594677738,"score_spread":0.2856376179562795,"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."}}