AI-predicted PT-symmetric magnets

Abstract

Parity-time-reversal-symmetric odd-parity antiferromagnetic (AFM1) materials are of interest for their symmetry-enabled quantum transport and optical effects. These materials host odd-parity terms in their band dispersion, leading to asymmetric energy bands and enabling responses such as the magnetopiezoelectric effect, nonreciprocal conductivity, and photocurrent generation. In addition, they may support a nonlinear spin Hall effect without spin-orbit coupling, offering an efficient route to spin current generation. We identify 23 candidate AFM1 materials by combining artificial intelligence, density functional theory (DFT), and symmetry analysis. Using a graph neural network model and incorporating AFM1-specific symmetry constraints, we screen Materials Project compounds for high-probability AFM1 candidates. DFT calculations show that AFM1 has the lowest energy among the tested magnetic configurations in 23 candidate materials. These include 3 experimentally verified AFM1 materials, 10 synthesized compounds with unknown magnetic structures, and 10 that are not yet synthesized.