Accelerated Discovery of CH4 Uptake Capacity Metal–Organic Frameworks Using Bayesian Optimization. Issue 3 (3rd February 2022) - Taw, Eric
Abstract: High‐throughput computational studies for discovery of metal–organic frameworks (MOFs) for separations and storage applications are often limited by the costs of computing thermodynamic quantities. Recent such studies at the time of writing may use ab initio results for a narrow selection of MOFs or empirical force‐field methods for larger selections. Here, a proof‐of‐concept study is conducted using Bayesian optimization on CH4 uptake capacity of hypothetical MOFs for an existing dataset (Wilmer et al., Nature Chem. 2012, 4, 83). It is shown that less than 0.1% of the database needs to be screened with the Bayesian optimization approach to recover the top candidate MOFs. This opens the possibility for efficient screening of MOF databases using accurate ab initio calculations for future adsorption studies on a minimal subset of MOFs. Furthermore, Bayesian optimization and the surrogate model presented here can offer interpretable material design insights and the framework will be applicable in the context of other target properties. Abstract : High‐throughput screening of new metal–organic framework materials for separations applications is challenged by computational cost. This study demonstrates that Bayesian optimization can dramatically reduce the computational time for discovery of frameworks with high CH4 uptake capacity, paving the way for more efficient screening of these materials using high accuracy methods.