Active Learning Guided Computational Discovery of 2D Materials with Large Spin Hall Conductivity
Two-dimensional (2D) materials are promising candidates for next-generation spintronic devices due to their tunable properties and potential for efficient spin-charge interconversion. However, discovering materials with intrinsically high spin Hall conductivity (SHC) is hindered by the vast chemical space and expensive nature of conventional experimental and first-principles methods. In this work, we employ an active learning framework to accelerate the discovery of high-SHC 2D materials. Machine learning (ML) models were trained on SHC values computed from density functional theory calculations, incorporating the Kubo formalism via tight-binding Hamiltonians constructed from maximally localized Wannier functions, with explicit treatment of spin-orbit coupling. Starting from random but chemically diverse 24 2D systems, the dataset was expanded to 41 cases (from an overall pool of around 2000 materials) over three active learning loops using an expected improvement acquisition strategy. The ML technique successfully identified several high SHC candidates with the best candidate exhibiting a SHC of 271.52 (hbar/e) Ohm^-1, nearly 23 times higher than the top performer in the initial round. Beyond candidate discovery, several features such as orbital symmetry near the Fermi energy, types of atomic species, material composition, covalent radii, and electronegativity of constituent atoms were found to play critical role in shaping the spin Hall response in 2D systems. The data generated is made publicly available to facilitate further advances in 2D spintronics.