Finding low-energy experimental metal-organic frameworks for carbon capture via Bayesian optimization in the manifold subspace of geometric descriptors

Carbon capture, utilization, and storage (CCUS) technologies are now being developed to meet the global net-zero emissions target. Due to their high surface area and high porosity, metal-organic frameworks (MOFs) are promising solid sorbent candidates for post-combustion carbon capture. Recent studies now use machine learning (ML) to accelerate the high-throughput screening of MOFs. However, most studies only rely on supervised learning to do structure-to-property predictions and offer little understanding about the MOF chemical space. In this paper, we aim to provide a more interpretable ML workflow for finding best-performing MOFs for carbon capture using manifold learning and Bayesian optimization. We posed an optimization problem whose objective is to find MOFs from the CoRE MOF 2019 database that minimize the required energy when simulated in a pressure-swing adsorber, subject to having high CO 2 purity and high CO 2 /N 2 selectivity. Different from existing literature, we introduce uniform manifold approximation and projection (UMAP) to first embed the pore geometry descriptors data in a 2-D manifold subspace, hence visualizing the MOF chemical space. We then explored the possibility of performing Bayesian optimization in the UMAP subspace to search MOFs more efficiently. Our results include a 2-D mapping of MOFs, a ranking of best MOFs, and an analysis of regions in the geometric descriptor subspace that led to best process performance. We hope that these contributions can help increase our understanding of the relationships between the material-level and process-level metrics of MOFs for carbon capture. • Explored a 2-D chemical space of MOFs by using UMAP on their geometric descriptors. • Evaluated 50 MOFs in a PSA simulation via Aspen Adsorption Suite. • Used BayesOpt on the subspace to find low-energy, high purity, and selective MOFs. • Identified narrow ranges of descriptors that lead to cost-effective carbon capture. • Best MOFs are in the UMAP neighborhoods of IFEPUG and NIKZAJ.