MOF-Select: Benchmarking Selection and Confidence in Conditional MOF Generation

Conditional generative models predict metal–organic framework (MOF) structures from specified building blocks. Their value for materials design depends on converting candidate pools into reliable recommendations, a transition that candidate coverage alone cannot assess. We introduce MOF-Select, a dataset and benchmark that makes this transition a measurable learning problem. MOF-Select aligns outputs from different generators under shared chemical conditions and provides candidate-level reference-recovery supervision. Controlled protocols separate available coverage from successful selection and acceptance under changes in generator source, chemistry, and sampling budget. The resource comprises 245,760 generation attempts and supports comparisons between heuristic and learned decision rules. Across three generators, the best selector in each generator/test setting leaves 7.85–24.74 percentage points of reference recovery unused at ten attempts per condition. Learning improves selection and confidence ranking on MOF-LLM pools, while larger pools can displace correct choices. Better confidence ranking does not ensure certified acceptance, and source-calibrated rules can incur high acceptance error after a generator change. MOF-Select establishes a common framework for evaluating and improving how generative coverage translates into reliable recommendations, making downstream decision quality an explicit target for progress in MOF generation.