{"id":"W4416299797","doi":"10.1021/acsnano.5c21643","title":"Adsorption Hysteresis Under Control: Tuning Host–Guest Interactions via a Genetic Algorithm","year":2025,"lang":"en","type":"article","venue":"ACS Nano","topic":"Mesoporous Materials and Catalysis","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"Office of Science; Canada First Research Excellence Fund; Basic Energy Sciences; National Energy Research Scientific Computing Center; U.S. Department of Energy; Infrastruktura PL-Grid; Academic Computer Centre Cyfronet, AGH University of Science and Technology; University of Toronto","keywords":"Hysteresis; Leverage (statistics); Nucleation; Adsorption; Monte Carlo method; Context (archaeology); Phase (matter); Desorption","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":[0.0005592651,0.000551661,0.0005295759,0.0004206945,0.0003150017,0.0005802208,0.0007470025,0.0009555457,0.0007292746],"category_scores_gemma":[0.001429995,0.0003292798,0.0003875337,0.0002743181,0.0006048587,0.0004255384,0.0004231615,0.0006244459,0.00009334609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006991096,"about_ca_system_score_gemma":0.0009352321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005388249,"about_ca_topic_score_gemma":0.004644089,"domain_scores_codex":[0.9998724,0.00003778849,0.000004489102,0.00003123192,0.0000259325,0.00002805611],"domain_scores_gemma":[0.999479,0.000379308,0.00004265748,0.00002092767,0.00005178625,0.00002631352],"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":[0.00005501862,0.00009243421,0.001000917,0.00002033902,0.00002210097,0.00003431439,0.00003430627,0.9779196,0.005525187,0.001642936,0.0001996307,0.01345329],"study_design_scores_gemma":[0.000008262364,0.00001470775,0.00005557348,0.000001192459,0.00000290626,0.000002660764,0.000003982329,0.9990672,0.0004378268,0.0003309344,0.00007283181,0.000001826196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5859413,0.0004807031,0.4061452,0.0005762392,0.00007782969,0.0001343537,0.00007104982,0.0009633832,0.005610056],"genre_scores_gemma":[0.8886292,0.0001164758,0.1096813,0.0001583012,0.0000135547,0.0001339007,0.00006479827,0.00005130156,0.001151182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005388249,"threshold_uncertainty_score":0.01071382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008780870022918696,"score_gpt":0.2462123001894761,"score_spread":0.2374314301665574,"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."}}