EFTGAN: Elemental features and transferring corrected data augmentation for the study of high-entropy alloys

Using machine learning to predict and design materials is an important mean of accelerating material development. One way to improve the accuracy of machine learning predictions is to introduce material structures as descriptors. However, the complexity of computing material structures limits the practical use of these models. To address this challenge and improve prediction accuracy in small data sets, we develop a generative network framework: Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks (EFTGAN). Combining the elemental convolution technique with Generative Adversarial Networks (GAN), EFTGAN provides a robust and efficient approach for generating data containing elemental and structural information that can be used not only for data augmentation to improve model accuracy, but also for prediction when the structures are unknown. Applying this framework to the FeNiCoCrMn/Pd high-entropy alloys, we successfully improve the prediction accuracy in a small data set and predict the concentration-dependent formation energies, lattices, and magnetic moments in quinary systems. This study provides a new algorithm to improve the performance and usability of deep learning with structures as inputs, which is effective and accurate for the prediction and development of materials for small data sets.