{"id":"W4248457740","doi":"10.26434/chemrxiv.12186681.v1","title":"Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Metal-Organic Frameworks: Synthesis and Applications","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Canadian Institute for Advanced Research; University of Ottawa; University of Toronto","funders":"Basic Energy Sciences; Natural Sciences and Engineering Research Council of Canada; Quest High Performance Computing; Office of Science; Ministère de l'Économie, de la Science et de l'Innovation - Québec; Compute Canada; École de technologie supérieure; Northwestern University; U.S. Department of Energy","keywords":"Autoencoder; Computer science; Nanoporous; Reticular connective tissue; Nanotechnology; Materials science; Artificial intelligence; Deep learning","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":[0.0003682109,0.0005823999,0.0005787947,0.0002626923,0.0001702494,0.0004668018,0.0005447182,0.001122527,0.001178027],"category_scores_gemma":[0.001069646,0.0005238611,0.0006742888,0.0001438368,0.0008483984,0.0004562511,0.0006931981,0.0005433953,0.0001547982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007168155,"about_ca_system_score_gemma":0.0006285387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002798134,"about_ca_topic_score_gemma":0.004361175,"domain_scores_codex":[0.9998974,0.00003115805,0.000003882402,0.00002718934,0.00002230827,0.00001809193],"domain_scores_gemma":[0.9996194,0.0002769587,0.00003235585,0.00001998419,0.00002717188,0.00002410567],"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":[0.00001492867,0.000009176007,0.00016216,0.00002144417,0.0000127096,0.00002231541,0.00001217213,0.9895517,0.001980324,0.004094163,0.0001618198,0.003957144],"study_design_scores_gemma":[0.000001752285,0.000003981332,0.00001232805,9.534396e-7,9.787381e-7,0.000002120653,0.000001355378,0.998943,0.0002157982,0.0007419132,0.00007485368,9.508218e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09937479,0.0004545593,0.8930098,0.000461956,0.00004127701,0.00004955447,0.0001428613,0.0005415835,0.005923704],"genre_scores_gemma":[0.8700048,0.0001630156,0.1258115,0.0002064337,0.0000173124,0.0001393819,0.000165832,0.0001086318,0.003382957],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002798134,"threshold_uncertainty_score":0.005563676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.040642738329056,"score_gpt":0.2364430074510832,"score_spread":0.1958002691220272,"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."}}