Physically Interpretable Descriptors Drive the Materials Design of Metal Hydrides for Hydrogen Storage

Abstract

Designing metal hydrides for hydrogen storage remains a longstanding challenge due to the vast compositional space and complex structure-property relationships. Herein, for the first time, we present physically interpretable models for predicting two key performance metrics, gravimetric hydrogen density w and equilibrium pressure P_(eq,RT) at room temperature, based on a minimal set of chemically meaningful descriptors. Using a rigorously curated dataset of 5,089 metal hydride compositions from our recently developed Digital Hydrogen Platform (DigHyd) based on large-scale data mining from available experimental literature of solid-state hydrogen storage materials, we systematically constructed over 1.6 million candidate models using combinations of scalar transformations and nonlinear link functions. The final closed-form models, derived from 2-3 descriptors each (e.g., atomic mass, electronegativity, molar density, and ionic filling factor), achieve predictive accuracies on par with state-of-the-art machine learning methods, while maintaining full physical transparency. Strikingly, descriptor-based design maps generated from these models reveal a fundamental trade-off between w and P_(eq,RT): saline-type hydrides, composed of light electropositive elements, offer high w but low P_(eq,RT), whereas interstitial-type hydrides based on heavier electronegative transition metals show the opposite trend. Notably, Beryllium (Be)-based systems, such as Be–Na alloys, emerge as rare candidates that simultaneously satisfy both performance metrics, attributed to the unique combination of light mass and high molar density for Be. Our models indicate that, while there remains room for improvement between the current state of solid-state hydrogen storage materials and the US-DOE targets, Be-based systems may offer renewed prospects for approaching these benchmarks. These results provide chemically intuitive guidelines for materials design and establish a scalable framework for the rational discovery of materials in complex chemical spaces. The methodology is broadly applicable and could serve as a template for data-driven exploration across other energy-relevant materials domains.