Enhanced phase prediction of high-entropy alloys through machine learning and data augmentation.

The phase structure information of high-entropy alloys (HEAs) is critical for their design and application, as different phase configurations are associated with distinct chemical and physical properties. However, the broad range of elements in HEAs presents significant challenges for precise experimental design and rational theoretical modeling and simulation. To address these challenges, machine learning (ML) methods have emerged as powerful tools for phase structure prediction. In this study, we use a dataset of 544 HEA configurations to predict phases, including 248 intermetallic, 131 solid solution, and 165 amorphous phases. To mitigate the limitations imposed by the small dataset size, we employ a Generative Adversarial Network (GAN) to augment the existing data. Our results show a significant improvement in model performance with data augmentation, achieving an average accuracy of 94.77% across ten random seeds. Validation on an independent dataset confirms the model's reliability and real-world applicability, achieving 100% prediction accuracy. We also predict FCC and BCC phases for SS HEAs based on elemental composition, achieving a peak accuracy of 98%. Furthermore, feature importance analysis identifies correlations between compositional features and phase formation tendencies, which are consistent with experimental observations. This work proposes an effective strategy to enhance the accuracy and generalizability of machine learning models in phase structure prediction, thus promoting the accelerated design of HEAs for a wide range of applications.