{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001438097,0.000209689,0.0003159465,0.00006127701,0.00028676,0.0001615346,0.0003319685,0.00006568181,0.0002881608],"category_scores_gemma":[0.0004765726,0.0001576097,0.00002657042,0.0002121596,0.0003764724,0.0002190105,0.00008627779,0.0001199768,0.00007679959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004219632,"about_ca_system_score_gemma":0.0001131788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001105153,"about_ca_topic_score_gemma":0.000006348481,"domain_scores_codex":[0.9979038,0.0006389849,0.0002488212,0.0006061841,0.0002389671,0.0003632053],"domain_scores_gemma":[0.9989278,0.0003039939,0.0001001372,0.000472176,0.0001102009,0.00008571473],"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":[0.00009618198,0.00003265172,0.1147391,0.00003393685,0.000008488802,0.00001837868,0.00009399836,0.006168096,0.8778684,0.0008169045,0.00004399184,0.00007990478],"study_design_scores_gemma":[0.0007833407,0.0001688421,0.1063097,0.00006852343,0.00005380576,0.000009684463,0.00004147166,0.006300039,0.8844481,0.001221393,0.0003130206,0.0002820866],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.776439,0.000150132,0.2216855,0.0007076362,0.0001511141,0.0003599924,0.000005467589,0.00009758393,0.0004035886],"genre_scores_gemma":[0.9789052,0.000004930444,0.019977,0.0007404719,0.0000230559,0.00006532118,9.377877e-7,0.0000136109,0.0002694903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2024662,"threshold_uncertainty_score":0.6427137,"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."}}