{"id":"W3025983901","doi":"10.1021/acsami.0c06110","title":"High-Throughput Screening of Antisolvents for the Deposition of High-Quality Perovskite Thin Films","year":2020,"lang":"en","type":"article","venue":"ACS Applied Materials & Interfaces","topic":"Perovskite Materials and Applications","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation; Ontario Centres of Excellence","keywords":"Materials science; Perovskite (structure); Chlorobenzene; Deposition (geology); Thin film; Characterization (materials science); High-throughput screening; Nanotechnology; Solvent; Throughput; Optoelectronics; Computer science; Chemical engineering; Catalysis; Organic chemistry; Bioinformatics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002556118,0.0002083064,0.0004811941,0.00002503168,0.00008210336,0.00007045946,0.000360868,0.00009723319,0.0001493314],"category_scores_gemma":[0.00001999567,0.0001648962,0.00003104078,0.00009592478,0.0000821033,0.00009772145,0.0001124742,0.00005650396,0.00001215151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009722791,"about_ca_system_score_gemma":0.000007019617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002286533,"about_ca_topic_score_gemma":0.00000662865,"domain_scores_codex":[0.9986578,0.00002658151,0.0007114312,0.0002335422,0.0001605346,0.0002100789],"domain_scores_gemma":[0.9992408,0.000135624,0.0002423009,0.0002893829,0.00005605145,0.00003590066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001228088,0.00002215533,0.000004451896,0.0003997516,0.00009913985,9.776799e-8,0.000407395,0.006948778,0.985122,0.005662504,0.0004981807,0.0007127893],"study_design_scores_gemma":[0.0004580303,0.00005503396,0.0006863114,0.00005303064,0.00006330416,5.488898e-7,0.0001907106,0.0001025703,0.9976102,0.0005465948,0.00007206971,0.0001616115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9744373,0.0001240849,0.0231408,0.0002397353,0.0002914127,0.0006209316,0.0009576275,0.0001306397,0.00005745203],"genre_scores_gemma":[0.9917549,0.00009336109,0.007562096,0.00008302424,0.0001404211,0.0001928281,0.0001239854,0.00004461438,0.000004722867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01731763,"threshold_uncertainty_score":0.6724271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0270421870744916,"score_gpt":0.2573154387349106,"score_spread":0.230273251660419,"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."}}