Peer-reviewedLicence Ouverte 2.0Computational Materials Science
Accelerating Phase Prediction via CALPHAD-Informed Machine Learning and Data Augmentation
- 1Federal University of São Carlos, Graduate Program in Materials Science and Engineering, São Carlos, SP, Brazil
- 2Univ. Grenoble Alpes, CNRS, Grenoble INP, SIMaP, F-38000 Grenoble, France
Related
- Hessian-Informed Conformation Augmentation for Potential Energy Surface Curvature in Machine Learning Interatomic Potentials
- Accelerating Proton Affinity Prediction with Multi-Fidelity Machine Learning
- Machine Learning Prediction of Transport Properties in Amorphous Polymer Electrolytes Using Chemically Informed Structural Descriptors
- Physics-Informed Deep Learning Surrogate for Mechanical-Response Prediction and Failure Assessment of High-Performance Polymer Composites
- Thermodynamically Constrained Deep Learning for Hydrogen Sorption Prediction: Overcoming Classical Isotherm Generalization Failure through Physics-Informed Cross-Lithology Transferability
BibTeX
@article{stoco2026accelerating,
title = {{Accelerating Phase Prediction via CALPHAD-Informed Machine Learning and Data Augmentation}},
author = {Caroline Binde Stoco and Guillaume Deffrennes and Yannick Champion and Francisco Gil Coury},
journal = {Computational Materials Science},
year = {2026},
month = jun,
doi = {10.1016/j.commatsci.2026.114834},
url = {https://hal.science/hal-05782178v1},
}
RIS
TY - JOUR
TI - Accelerating Phase Prediction via CALPHAD-Informed Machine Learning and Data Augmentation
AU - Caroline Binde Stoco
AU - Guillaume Deffrennes
AU - Yannick Champion
AU - Francisco Gil Coury
PY - 2026
DA - 2026/06/04
JO - Computational Materials Science
DO - 10.1016/j.commatsci.2026.114834
UR - https://hal.science/hal-05782178v1
SN - 0927-0256
SN - 1879-0801
ER -