Minimal crystallographic descriptors of sorption properties in hypothetical MOFs and role in sequential learning optimization

• Published in: npj computational materials Vol. 8; no. 1; pp. 1 - 14
• Main Authors: Trezza, Giovanni, Bergamasco, Luca, Fasano, Matteo, Chiavazzo, Eliodoro
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 03.06.2022; Nature Portfolio
• Subjects: Adsorption; Algorithms; Carbon dioxide; Crystallography; Energy conversion; Energy storage; Experiments; Heat; Machine learning; Metal-organic frameworks; Optimization; Sorbents; Sorption; Specific energy; Thermal energy
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-022-00806-7

Abstract We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO 2 and H 2 O. A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified. In energy transformations, thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values, which is often only partially accessible, hence possibly preventing the computation of desired objective functions. We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area. We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.