{"id":"W4410423362","doi":"10.1139/cjc-2025-0027","title":"(Coulomb) Local potential energy density–supramolecular energy (LPED–SME) machine learning prediction—a web application to obtain the local SME from simple inputs","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Chemistry","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Chemistry; Simple (philosophy); Energy (signal processing); Supramolecular chemistry; Coulomb; Electric potential energy; Molecule; Organic chemistry; Quantum mechanics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006869318,0.001012667,0.0008841479,0.0008600824,0.0003617569,0.0006881781,0.001647677,0.0009588417,0.02902859],"category_scores_gemma":[0.002384681,0.0004846206,0.0009271111,0.000589357,0.0002513907,0.001042537,0.0011646,0.001236989,0.009241981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003800707,"about_ca_system_score_gemma":0.0006337684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002113729,"about_ca_topic_score_gemma":0.00296005,"domain_scores_codex":[0.9998013,0.00002740053,0.00001117687,0.00005768696,0.00008279012,0.00001951317],"domain_scores_gemma":[0.9995311,0.0002511455,0.00004304443,0.00006598386,0.00007766914,0.00003108975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006670634,0.0006918372,0.01624224,0.001688494,0.0004606773,0.0009446081,0.0002933095,0.301084,0.03140643,0.02274056,0.2359488,0.387832],"study_design_scores_gemma":[0.00005453863,0.00004308025,0.001262573,0.00002922845,0.00001085989,0.0001088532,0.000013246,0.9657461,0.01276745,0.007715137,0.01221673,0.00003222988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04653565,0.0003550279,0.6080599,0.0003910317,0.0001261243,0.0002419095,0.0198712,0.3147939,0.009625211],"genre_scores_gemma":[0.4113421,0.0004326904,0.5261149,0.0005732979,0.000100771,0.001398317,0.02909134,0.01883741,0.01210912],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02902859,"threshold_uncertainty_score":0.09711033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002360136249086203,"score_gpt":0.193474320802257,"score_spread":0.1911141845531708,"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."}}