{"id":"W4399560162","doi":"10.26434/chemrxiv-2023-j1szt-v2","title":"Navigating Materials Space with ML-Generated Electronic Fingerprints","year":2024,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electronic structure; Computer science; Similarity (geometry); Fingerprint (computing); Property (philosophy); Photovoltaics; Data mining; Materials science; Artificial intelligence; Computational chemistry; 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":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002241657,0.0008131265,0.0008875842,0.00008035641,0.0002598579,0.001647748,0.001473557,0.0004498841,0.003984261],"category_scores_gemma":[0.0002837155,0.0006597839,0.0001066618,0.0003889502,0.0003454104,0.000130956,0.002567881,0.001790112,0.002826099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003552698,"about_ca_system_score_gemma":0.000712114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005338727,"about_ca_topic_score_gemma":0.00003407086,"domain_scores_codex":[0.9949211,0.000321427,0.0007407112,0.001931135,0.0007997605,0.001285897],"domain_scores_gemma":[0.9975406,0.0001045879,0.0006039431,0.00135182,0.000198867,0.000200179],"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":[0.00004970487,0.00003106004,0.00007364562,0.000751888,0.00002839339,0.00006416496,0.0004845392,0.004566625,0.9925554,0.0008480664,0.0003943405,0.0001521954],"study_design_scores_gemma":[0.0001829928,0.00008020869,0.0001038409,0.001554356,0.00008310542,0.0000612773,0.00003089552,0.001512687,0.9904159,0.004395505,0.0007171087,0.0008621728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920912,0.0004697534,0.0004585651,0.001083995,0.003065143,0.0006249678,0.00003380354,0.001051063,0.001121465],"genre_scores_gemma":[0.9878047,0.00004093667,0.009691696,0.0001524649,0.0008416089,0.000236706,0.0000950056,0.0001833007,0.0009535792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009233131,"threshold_uncertainty_score":0.9995853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01095065098764463,"score_gpt":0.2797430297179156,"score_spread":0.268792378730271,"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."}}