Deep Learning Sheds Light on Integer and Fractional Topological Insulators Xiang Li,1, ∗Yixiao Chen,1 Bohao Li,2 Haoxiang Chen,1, 3 Fengcheng Wu,2, † Ji Chen,3, ‡ and Weiluo Ren1, § 1ByteDance Research 2School of Physics and Technology, Wuhan University 3School of Physics, Peking University (Dated: March 18, 2025) Electronic topological phases of matter, characterized by robust boundary states derived from topologically nontrivial bulk states, are pivotal for next-generation electronic devices. However, understanding their complex quantum phases, especially at larger scales and fractional fillings with strong electron correlations, has long posed a formidable computational challenge. Here, we employ a deep learning framework to express the many-body wavefunction of topological states in twisted MoTe2 systems, where diverse topological states are observed. Leveraging neural networks, we demonstrate the ability to identify and characterize topological phases, including the integer and fractional Chern insulators as well as the Z2 topological insulators. […]