Data-Driven Deep Generative Design of Stable Spintronic Materials

Abstract

Discovering novel magnetic materials is essential for advancing the spintronic technology with significant applications in data communication, data storage, quantum computing, and etc. While Density functional theory (DFT) has been widely used for designing materials, its high computational demand for estimating the magnetic ground states of even a single material limits its ability to explore the vast chemical design space for finding the right materials for spintronic applications. In this work, we developed a computational framework combining generative adversarial networks (GAN), machine learning (ML) classifiers, and DFT for de novo magnetic material discovery. We used the CubicGAN generative crystal structure design model for creating new ternary cubic structures. Machine learning classifiers were developed with around 90% accuracy to screen candidate ternary magnetic materials, which are then subject to DFT based stability validation. Our calculations discovered and confirmed that Na6TcO6, K6TcO6, and BaCuF6 are stable ferromagnetic compounds, while Rb6IrO6 is a stable antiferromagnetic material. Moreover, Na6TcO6 and BaCuF6 are found to be half metals that are highly favorable for spintronic applications. Due to the structural differences, A6MO6 materials have a higher thermal capacity (Cv) compared to BaCuF6. At 300 K temperature, Cv of A6MO6 materials is around 1100 J/K.mol and that of BaCuF6 is about 176 J/K.mol. This work demonstrates the promising potential of deep generative design for discovering novel functional materials.