{"id":"W4225964801","doi":"10.26434/chemrxiv-2021-ql3b7-v3","title":"Bridging the Gap between H- and J-Aggregates: Classification and Supramolecular Tunability for Excitonic Band Structures in 2-Dimensional Molecular Aggregates","year":2022,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Perovskite Materials and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Division of Chemistry; Universität Leipzig; National Science Foundation","keywords":"J-aggregate; Chemical physics; Exciton; Bridging (networking); Spectral line; Monomer; Band gap; Aggregate (composite); Photonics; Quantum; Absorption (acoustics); Optoelectronics; Supramolecular chemistry; Materials science; Molecular physics; Physics; Condensed matter physics; Nanotechnology; Molecule; Optics; Quantum mechanics; Polymer; Computer science","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":[0.0001631904,0.0001846153,0.0001656804,0.0004061017,0.0003713684,0.0006251721,0.000193979,0.0003090397,0.0005753425],"category_scores_gemma":[0.0001638723,0.000162738,0.0001541272,0.0002438791,0.0004537615,0.0005583719,0.0003254945,0.0003479369,0.0001101321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002144984,"about_ca_system_score_gemma":0.00007083394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001261548,"about_ca_topic_score_gemma":0.0001883456,"domain_scores_codex":[0.999912,0.00001134714,0.000008183375,0.0000289483,0.00002079007,0.00001866492],"domain_scores_gemma":[0.9998924,0.00002380442,0.00003182022,0.00002259264,0.00001136198,0.0000180574],"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":[0.0001280607,0.00004042282,0.002011045,0.00007598053,0.00001421665,0.0001929569,0.0003379464,0.002280927,0.9734607,0.01278039,0.0001623858,0.008514859],"study_design_scores_gemma":[0.00001858711,0.0003042878,0.02057648,0.00002433108,0.00003423646,0.0007915265,0.0004064289,0.03453875,0.918285,0.01950532,0.005456842,0.00005822007],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878977,0.0006481062,0.008362321,0.00007201166,0.00001201586,0.000008074092,0.00006574555,0.00008185476,0.00285218],"genre_scores_gemma":[0.9963598,0.0001647883,0.002839126,0.00002453149,0.000005172089,0.00001108618,0.00006545374,0.00001355401,0.0005165411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0006251721,"threshold_uncertainty_score":0.001924694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02516868918494864,"score_gpt":0.261389456854352,"score_spread":0.2362207676694034,"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."}}