Target-driven discovery of metal hydrides using a high-throughput computational framework: from operational targets to sustainability assessment
Abstract
Metal hydrides based on multi-principal element alloys (MPEAs) offer tunable hydrogen storage properties, but their vast compositional space poses significant design challenges. A high-throughput computational strategy is proposed to identify alloys under predefined operating conditions, demonstrated here for hydrogen gas separation. CALPHAD equilibrium calculations were performed in the Ti-V-Cr-Nb system using a Python TM script with PanPython (Pandat TM SDK). Single-phase body-centered cubic (BCC) alloys were identified and evaluated using a computational framework to predict pressure-composition-temperature (PCT) diagrams. Candidates were screened based on operational requirements and ranked using multi-criteria decision analysis (MCDA), considering economic, environmental, and societal indicators. The highest-ranked alloy, Ti 20 V 12.5 Cr 20 Nb 47.5 , was synthesized and characterized. Hydrogen storage measurements using a volumetric apparatus confirmed reversible hydrogen uptake of 1.2 H/M (1.68 wt.%) under the targeted operating conditions. This integrated framework enables the identification of hydrides operating under predefined conditions while supporting sustainability-driven decision-making, paving the way for accelerated discovery of hydrogen materials and the advancement of sustainable energy technologies.