2 papers
cond-mat.mtrl-sci2026
Machine-Learning-Compatible CALPHAD-type Optimization from Phase Equilibria by Auto-differentiation
Wenhao Zhang, Jean-Claude Crivello, Yusuke Matsuoka +2
To accurately determine phase boundaries and phase transitions, thermodynamic models that describe phase free energies often have to be optimized based on experimentally observed p…
cond-mat.mtrl-sci2025
Data-efficient machine-learning of complex Fe-Mo intermetallics using domain knowledge of chemistry and crystallography
Mariano Forti, Alesya Malakhova, Yury Lysogorskiy +5
Atomistic simulations of multi-component systems require accurate descriptions of interatomic interactions to resolve details in the energy of competing phases. A particularly chal…