Accelerated Discovery of CH 4 Uptake Capacity Metal–Organic Frameworks Using Bayesian Optimization
[1];
[2]
- Department of Chemical and Biomolecular Engineering University of California, Berkeley Berkeley CA 94720 USA, Materials Science Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA
- Materials Science Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA, Department of Physics University of California, Berkeley Berkeley CA 94720 USA, Kavli Energy NanoScience Institute at Berkeley Berkeley CA 94720 USA
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 CH 4 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.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- SC0019992
- OSTI ID:
- 1854411
- Journal Information:
- Advanced Theory and Simulations, Journal Name: Advanced Theory and Simulations Journal Issue: 3 Vol. 5; ISSN 2513-0390
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- Germany
- Language:
- English
Similar Records
High‐Capacity Splitting of Mono‐ and Dibranched Hexane Isomers by a Robust Zinc‐Based Metal–Organic Framework
In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm