Crystal Fractional Graph Neural Network for Energy Prediction of High-Entropy Alloys

ArXi:2605.08103v1 Announce Type: cross High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic configurations. In this paper, we propose crystal fractional graph neural network for predicting the energy of high-entropy alloys by explicitly integrating both local atomic environments and global compositional information.