{"id":"W6979318945","doi":"","title":"Generative Molecular Design with Steerable and Granular Synthesizability Control","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Flexibility (engineering); Generative grammar; Generative model; Benchmark (surveying); Constraint (computer-aided design); Degrees of freedom (physics and chemistry); Control (management); Molecule","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0008895556,0.000830625,0.0006622198,0.0005332899,0.0004448199,0.001289222,0.001022877,0.0009792439,0.004184737],"category_scores_gemma":[0.001998276,0.000715751,0.001151474,0.0003403427,0.001659597,0.0008604504,0.001776017,0.001173397,0.0007787618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008793163,"about_ca_system_score_gemma":0.0008405257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009895845,"about_ca_topic_score_gemma":0.001470233,"domain_scores_codex":[0.9994701,0.0001236827,0.0000262891,0.000128984,0.0001841008,0.00006683364],"domain_scores_gemma":[0.9992635,0.0003898348,0.00008954826,0.000141995,0.00006484408,0.00005033801],"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":[0.0000758151,0.00005725958,0.0007072486,0.0001701479,0.00003051588,0.0001774329,0.0001114762,0.8713717,0.02508719,0.07420578,0.0008023778,0.02720289],"study_design_scores_gemma":[0.00003488959,0.00007718244,0.00009457717,0.00001634955,0.00001587793,0.00005507013,0.00002066288,0.9599925,0.006026763,0.02937943,0.004269195,0.00001748401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0294977,0.0003533408,0.9600565,0.0002286676,0.00005726343,0.0001198214,0.0001156746,0.00105455,0.008516419],"genre_scores_gemma":[0.6135564,0.0005361215,0.377915,0.0002835427,0.00002988387,0.0005353848,0.0002911261,0.0004380774,0.006414449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004184737,"threshold_uncertainty_score":0.01399928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231243601071128,"score_gpt":0.2422473061820953,"score_spread":0.229934870171384,"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."}}