Unsupervised classification of non-Hermitian topological phases under symmetries.

ABSTRACT: The integration of machine learning into fundamental science has opened new avenues for addressing long-standing challenges rooted in mathematical limitations. For instance, while topological invariants are essential for characterizing topological phases of matter, no single invariant is universally applicable. This limitation explains why, over decades of classifying topological phases-primarily in Hermitian systems-many phases initially deemed 'trivial' were later recognized as topological. Recently, the discovery of non-Hermitian band topology has driven substantial efforts in non-Hermitian topological classification, leading to the development of new topological invariants. However, these invariants still fail to capture all non-Hermitian topological features. Here, without relying on

SUBMITTER: Long Y 

PROVIDER: S-EPMC12796827 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature