Efficiently predicting pressure-composition-temperature diagrams to discover low-stability metal hydrides
A first principles sampling-based approach to PCT prediction in compositionally complex alloys is employed to improve out-of-distribution prediction accuracy compared to data-driven models.
Institutions
- Uppsala University (SE)
- Lawrence Livermore National Laboratory (US)
- Centre National de la Recherche Scientifique (FR)
- University of Nottingham (GB)
- Lawrence Berkeley National Laboratory (US)
- Sandia National Laboratories California (US)
- Université Paris-Est Créteil (FR)
- Institut de Chimie et des Matériaux Paris-Est (FR)
- National Institute of Advanced Industrial Science and Technology (JP)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1039/d5ta06645j
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00