Leakage-aware machine learning for data-driven performance prediction of metal-organic framework systems.

Metal-organic frameworks (MOFs) are highly tunable porous materials whose performance is governed by complex interactions among structural, chemical, material, and operating variables. This study develops a leakage-aware, data-driven framework for predicting two distinct MOF performance endpoints: loading capacity and cell viability. The curated datasets comprised 161 loading-capacity observations described by 110 metal, ligand, functionality, and molecular-fingerprint variables, and 444 cell-viability observations described by 25 MOF, cell-category, and exposure variables. Histogram-based gradient boosting regression (HGBR) and partial least squares regression (PLSR) were optimized using differential evolution within five-fold grouped cross-validation. Exact duplicate records were constrained to the same data partition to prevent information leakage, while an additional source-held-out test was employed to assess transferability to publications not represented during model training. On the duplicate-safe 20% holdout, HGBR achieved the strongest predictive performance for cell viability (