Phase prediction for high-entropy alloys using generative adversarial network and active learning based on small datasets

In this paper, a new machine learning (ML) model combining conditional generative adversarial networks (CGANs) and active learning (AL) is proposed to predict the body-centered cubic (BCC) phase, face-centered cubic (FCC) phase, and BCC+FCC phase of high-entropy alloys (HEAs). Considering the lack of data, CGANs are introduced for data augmentation, and AL can achieve high prediction accuracy under a small sample size owing to its special sample selection strategy. Therefore, we propose an ML framework combining CGAN and AL to predict the phase of HEAs. The arithmetic optimization algorithm (AOA) is introduced to improve the artificial neural network (ANN). AOA can overcome the problem of falling into the locally optimal solution for the ANN and reduce the number of training iterations. The AOA-optimized ANN model trained by the AL sample selection strategy achieved high prediction accuracy on the test set. To improve the performance and interpretability of the model, domain knowledge is incorporated into the feature selection. Additionally, considering that the proposed method can alleviate the problem caused by the shortage of experimental data, it can be applied to predictions based on small datasets in other fields.