Papers › Unsupervised Classification of Non-Hermitian Topological Phases under Symmetries
Unsupervised Classification of Non-Hermitian Topological Phases under Symmetries
Yang Long, Haoran Xue, Baile Zhang
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The integration of artificial intelligence (AI) into fundamental science has opened new possibilities to address long-standing scientific challenges rooted in mathematical limitations. For example, topological invariants are used to characterize topology, but there is no universally applicable one. This limitation explains why, in the past decades-long classification of topological phases of matter -- mainly focused on Hermitian systems -- many phases initially classified ``trivial" were later identified as topological. Recently, the discovery of non-Hermitian band topology has spurred substantial efforts in non-Hermitian topological classification, including the development of new topological invariants. However, such classifications similarly risk overlooking key topological features. Here, without relying on any topological invariant, we develop an AI-based unsupervised classification of symmetry-protected non-Hermitian topological phases. This algorithm distinguishes topological differences among non-Hermitian Hamiltonians with symmetries, and constructs, in an unsupervised manner, a topological periodic table for non-Hermitian systems. Additionally, it can account for the boundary effects, enabling the exploration of open-boundary effects on the topological phase diagram. These results introduce an unsupervised approach for classifying symmetry-protected non-Hermitian topological phases without omission and provide valuable guidance for the development of theories and experiments.
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