{"id":"W4233109831","doi":"10.26434/chemrxiv.12186681","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":44,"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":"Reticular connective tissue; Nanoporous; Computer science; Autoencoder; 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":[0.0003489501,0.000584037,0.0005897313,0.0002482803,0.0001903527,0.0004720719,0.0006163131,0.001078777,0.001367622],"category_scores_gemma":[0.001012676,0.0004789442,0.000696526,0.0001448267,0.0007851578,0.0004453035,0.0006860429,0.0005710783,0.0002074469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007386137,"about_ca_system_score_gemma":0.0006350169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002611661,"about_ca_topic_score_gemma":0.004680407,"domain_scores_codex":[0.9998864,0.00003028232,0.000004314636,0.00003238913,0.00002580315,0.00002082578],"domain_scores_gemma":[0.9996294,0.0002644923,0.00003194203,0.00002067176,0.00002743483,0.00002612468],"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":[0.00001780672,0.00001228154,0.0002019474,0.0000303132,0.00001600184,0.00003388474,0.0000143835,0.984742,0.003111929,0.005120495,0.0002365548,0.006462364],"study_design_scores_gemma":[0.00000174518,0.000005553868,0.00001312376,0.000001163082,0.00000136193,0.000003109483,0.000001583517,0.998745,0.0003699709,0.0007418562,0.0001144123,0.00000115605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09004689,0.000540529,0.9013445,0.0004099081,0.00004535042,0.00004883645,0.0001436969,0.0007136973,0.006706593],"genre_scores_gemma":[0.8523532,0.0001840014,0.1433057,0.0002344972,0.0000174732,0.0001434358,0.0001961935,0.0001343315,0.003431188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002611661,"threshold_uncertainty_score":0.005359113,"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."}}