Optimizing MOF properties for seasonal heat storage: a machine learning approach

In the quest to enhance thermochemical energy storage using promising sorbents, this work presents a study on the optimization of Metal Organic Frameworks (MOFs) properties for gas sorption, with a focus on CO2 and H2O adsorption. Through the analysis of crystallographic descriptors, the study aims to streamline the selection of MOFs that could potentially exceed the performance of existing water sorbent pairs. A comprehensive comparison of sequential learning (SL) algorithms reveals a method for identifying the minimal set of descriptors that influence adsorption properties of MOFs. The protocol involves constructing and training machine learning (ML) models to determine the number of influential descriptors and utilizing SHAP analysis to evaluate their importance. Findings suggest that including only these critical descriptors in the exploration space reduces computational load. Notably, the COMBO and the FUELS algorithms consistently outshine random guessing, validating their efficacy in materials optimization. The challenge of accessing full adsorption properties across the entire coverage range is addressed by a computational screening procedure requiring minimal input data. This method suggests that some vanadium based MOFs, originally designed for different purposes, could surpass the current leading compounds for thermal energy storage, primarily due to their optimal Henry coefficient values for water adsorption.