High-entropy alloys (HEAs) have emerged as promising candidates for solid-state hydrogen storage, yet the vast compositional space poses a major challenge for targeted materials discovery. Here, we develop a deep generative framework based on generative adversarial networks (GANs) and conditional GANs (CGANs) to enable the inverse design of HEAs with optimized hydride formation enthalpy and hydrogen storage capacity. The GAN accurately captures the underlying compositional distributions, achieving high novelty and diversity while reproducing known chemical trends and empirical phase-formation rules. By incorporating property labels as conditional inputs, the CGAN allows directional control over the generation process, steering the model toward alloys with targeted enthalpy and capacity ranges. Density functional theory (DFT) calculations are performed to validate a representative GAN-generated composition, Mn18V2Fe16Ti18, confirming its structural stability and hydrogen uptake, in good agreement with machine-learning predictions. These results demonstrate that GAN model can autonomously learn distribution characteristics of data set and efficiently explore unexplored regions of the HEAs design space, providing a powerful strategy for the inverse design of advanced hydrogen storage materials.
Zhao et al. (Mon,) studied this question.