A Machine-Learning-Accelerated Approach for Room-Temperature Phase Diagram Predictions
} assemblage to {LiGaSn, LiGa, LiSn} at approximately 230 K. X-ray diffraction of two synthesized compositions supports the predicted room-temperature assemblages. The approach offers a practical route for scalable finite-temperature phase-diagram construction and thermodynamic screening of intermetallic systems.
Authors
- Chenji Hu (ORCID: https://orcid.org/0000-0002-0713-5499)
- Hong Zhu (ORCID: https://orcid.org/0000-0001-7919-5661)
- Z-F Yu
- J J Lu
- S M Chen
- Yixiao Zhang (ORCID: https://orcid.org/0000-0002-2807-3936)
- Zhiwei Yu
- Guozhong Lu (ORCID: https://orcid.org/0009-0000-4360-2819)
- Yuping Huang (ORCID: https://orcid.org/0000-0001-7942-9704)
- Liwei Chen (ORCID: https://orcid.org/0000-0003-4160-9771)
- Yucheng Fu
Institutions
- Shanghai Jiao Tong University (CN)
- Global College (CY)
Publication Details
- Journal
- The Journal of Physical Chemistry Letters
- Published
- 2026-07-27
- DOI
- https://doi.org/10.1021/acs.jpclett.6c01568
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality
- National Key Research and Development Program of China