Discovery of New Air Separation Metal-Organic Frameworks via Bayesian Optimization

The discovery of new metal-organic frameworks (MOFs) for gas separations is highly labor- and time-intensive. Recent studies propose using high-throughput computational screening, though this approach requires a trade-off between computational expense and exploration of a wide chemical space. Because density functional theory (DFT) calculations are expensive and often require manual intervention, DFT is often limited to a small set of structurally similar MOFs. Here, we mitigate the expense-exploration trade-off by using Bayesian optimization to minimize the number of expensive yet accurate DFT calculations required to evaluate a new MOF for air separation. We first benchmark the ability for PBE-D3+U calculations to recover experimentally determined enthalpies of adsorption of O₂, then proceed to calculate the same for promising candidate MOFs proposed by our Bayesian optimization procedure. By identifying MOFs that have a binding enthalpy of O₂ around -45 kJ/mol, we propose a candidate material that is predicted to dramatically reduce the energy cost of air separation compared to standard industrial adsorbents.