Accelerating the Design of High-Entropy Alloys for Hydrogen Storage via Generative Adversarial Networks
, 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.
Authors
- Shiyin Ma (ORCID: https://orcid.org/0000-0001-9763-9844)
- Chenglong Qin (ORCID: https://orcid.org/0000-0003-1653-3170)
- Z.T. Fan
- Qunchao Fan
- L. Zhao
Institutions
- Xihua University (CN)
- Sichuan University (CN)
- Institute of High Performance Computing (SG)
Publication Details
- Journal
- Inorganic Chemistry
- Published
- 2026-04-06
- DOI
- https://doi.org/10.1021/acs.inorgchem.6c00715
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Sichuan Province
- Sichuan Province Science and Technology Support Program