Abstract Accurate first-principles-based prediction of the pressure-composition-temperature (PCT) relationships of metal hydrides can enable predictive optimization of hydrogen capacities and pressures. In this work, we introduce a novel computational framework that integrates density functional theory (DFT) with a Python-based PCT Simulation Toolkit to predict PCT diagrams with high accuracy. By using only structural input data from the metallic phase, this toolkit automates the detection of interstitial voids, generates input files for DFT calculations, and constructs thermodynamic models based on para-equilibrium principles. We validate this approach across five major metal-hydride classes – BCC and FCC alloys, AB 5 , AB 2 , and AB compounds - and demonstrate that even with minimal computational effort, key hydrogen sorption characteristics can be reliably determined. Using the PBE functional without vibrational contribution, our results show that hydrogen capacity predictions achieve a mean accuracy of 95%, while sorption pressures are modeled within one order of magnitude of experimental values. Furthermore, our method can implicitly account for the phase transition in metal hydrides and can reliably model multicomponent alloys with representative alloys of lesser chemical complexity. This framework enables rapid and accurate exploration of metal hydrides to design alloys for new applications.
Hannappel et al. (2025) studied this question.