Data-driven deep generative design of stable spintronic materials

Discovering novel magnetic materials is essential for advancing the spintronic technology with significant applications in data communication, data storage, quantum computing, 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 to find the right materials for spintronic applications. In this work, we developed a computational framework combining generative adversarial networks (GANs), 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 were then subjected 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. All these materials have zero energy above hull. Moreover, Na6TcO6 and BaCuF6 are found to be half metals that are highly favorable for spintronic applications. Due to the structural differences, the A6MO6 materials have a higher thermal capacity (Cv) compared to BaCuF6. At 300 K temperature, the Cv of the A6MO6 materials is around 1100 J K-1 mol-1 and that of BaCuF6 is about 176 J K-1 mol-1. This work demonstrates the promising potential of deep generative design for discovering novel functional materials. A computational framework that integrates generative adversarial networks and machine learning classifiers to enable the discovery of novel magnetic materials.