Bayesian Optimization Approach for Discovery of High-Capacity Small-Molecule Adsorption in Metal-Organic Frameworks*

Metal-organic frameworks, due to their highly porous structures, have emerged as a promising class of small-molecule adsorbent materials for a variety of separations, storage, and usage applications. While it is possible to construct viable hypothetical MOFs (hMOFs) from known metal nodes and organic linkers, it is computationally expensive to calculate at a high accuracy the small molecule uptake capacity of MOF structures. Using ~51,000 hypothetical MOF structures and data calculated from [1] for CH₄, we show it is possible to identify candidates for high-performance CH₄ adsorbents by calculating uptake capacities for <1% of the database using Bayesian optimization. Furthermore, we show that building chemical intuition into the surrogate model and including structural characteristics dramatically improves the performance and interpretability of the optimization process. The applicability of our Bayesian approach and workflow to molecules beyond CH₄ and other hypothetical adsorbents is discussed.
[1] Wilmer, C., et al., Nat. Chem 4, 83–89 (2012).