{"id":"W4410854799","doi":"10.1021/acsnano.5c00670","title":"Using Machine Learning to Fast-Track Peptide Nanomaterial Discovery","year":2025,"lang":"en","type":"review","venue":"ACS Nano","topic":"Supramolecular Self-Assembly in Materials","field":"Materials Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Hrvatska Zaklada za Znanost; Sveučilište u Rijeci","keywords":"Track (disk drive); Nanomaterials; Nanotechnology; Peptide; Computer science; Artificial intelligence; Materials science; Chemistry; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001158781,0.001006662,0.002741977,0.0005014706,0.0003538251,0.00139025,0.00147354,0.0005707114,0.0004959506],"category_scores_gemma":[0.0007841061,0.0008770083,0.0004893551,0.0007052119,0.00007232817,0.0005152727,0.00126955,0.0002994668,0.001762698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004203131,"about_ca_system_score_gemma":0.0007772695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002804586,"about_ca_topic_score_gemma":0.00002022858,"domain_scores_codex":[0.9944927,0.001183694,0.001397764,0.001313251,0.000616057,0.0009965663],"domain_scores_gemma":[0.9974754,0.0003632405,0.0007012834,0.001155877,0.0001107765,0.0001933527],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005085174,0.0001212617,0.00000619818,0.01827595,0.0001369602,0.0001828506,0.000145905,0.0000747053,0.9365552,0.0001595054,0.0006778283,0.04361278],"study_design_scores_gemma":[0.0002251632,0.000101621,6.564943e-7,0.01422357,0.0008072692,0.0001218269,0.00002106278,0.000003282198,0.1227913,0.00003475231,0.8605197,0.001149841],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.04799111,0.9411152,0.0002686201,0.00001643217,0.007392737,0.001771408,0.0004950776,0.0004262513,0.0005231788],"genre_scores_gemma":[0.0003977618,0.9869997,0.006682868,0.0001646319,0.0009893922,0.0002491946,0.0003333521,0.0002607103,0.003922428],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8598419,"threshold_uncertainty_score":0.9996464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03591247525494667,"score_gpt":0.3349464887680234,"score_spread":0.2990340135130767,"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."}}