2 papers
physics.comp-ph2026
Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys
Takanori Kotama, Yang Huang
High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic configurations. In this paper, we pr…
cond-mat.mtrl-sci2025
Decoding the Stability of Transition-Metal Alloys with Theory-infused Deep Learning
Yang Huang, Shih-Han Wang, Shuyi Cao +2
We introduce an interpretable deep learning framework that predicts the cohesive energy of transition-metal alloys (TMAs) by embedding cohesion theory within graph neural networks…