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, 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 (C_v) compared to BaCuF₆. At 300 K temperature, the C_v 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.