Machine learning-driven identification of key factors influencing hydrogen storage performance in metal hydrides

Abstract

Data-driven machine learning (ML) methods are now indispensable for analyzing and predicting material properties, offering critical insights for the design and synthesis of new materials. This study leverages ML approaches to identify and analyze the key factors influencing the thermodynamic properties of metal hydrides. Utilizing a comprehensive dataset of 721 metal hydrides from the experimental literature, we developed ML models that demonstrated robust predictive capabilities, achieving a coefficient of determination (R2) of 0.912 for equilibrium hydrogen plateau pressure and 0.881 for dehydrogenation enthalpy. Feature importance analysis revealed three critical descriptors, specific volume per atom for a given composition, magnetic transition metal fraction, and mean shear modulus, that significantly impact the thermodynamic properties of hydrogen storage materials. This research provides valuable insights into the fundamental factors governing hydrogen storage in metal hydrides and presents a promising approach for the design and discovery of novel hydrogen storage materials.