Discovery of new topological insulators and semimetals using deep generative models

Abstract

Topological materials possess unique electronic properties and hold immense attraction to both fundamental physics research and practical applications. Over the past decades, the discovery of new topological materials has relied on the symmetry-based analysis of the quantum wave function. In this study, we propose an efficient inverse design method CTMT (CTMT: CDVAE, Topogivity, interatomic potentials (IAPs) as realized in M3GNet, and TQC) utilizing deep generative machine learning models to discover novel topological insulators and semimetals in a much-fast and low-cost manner. This method covers the entire process of new crystal structure generation, heuristic rule screening, fast stability estimation, and topology type diagnosis, resulting in 4 topological insulators and 16 topological semimetals. Especially, the newly discovered topological materials include several chiral Kramers-Weyl fermion semimetals and chiral materials with low symmetry, whose topology is previously considered challenging to discern. These findings demonstrate the capability of CTMT in discovering topological materials and its great potential for data-driven inverse design of advanced functional materials.