Non-Hermitian topological phases can produce some remarkable properties, compared with
their Hermitian counterpart, such as the breakdown of conventional bulk-boundary …

The study of topological properties by machine learning approaches has attracted
considerable interest recently. Here we propose machine learning the topological invariants …

Non-Hermitian topological phases have gained widespread interest due to their
unconventional properties, which have no Hermitian counterparts. In this work, we propose …

The integration of artificial intelligence (AI) into fundamental science has opened new
possibilities to address long-standing scientific challenges rooted in mathematical …

We apply supervised machine learning to study the topological states of one-dimensional
non-Hermitian systems. Unlike Hermitian systems, the winding number of such non …

We propose a scheme to realize various non-Hermitian topological phases in a topolectrical
(TE) circuit network consisting of resistors, inductors, and capacitors. These phases are …

Topological phases of matter are conventionally characterized by the bulk‐boundary
correspondence in Hermitian systems. The topological invariant of the bulk in d dimensions …

Machine learning has been used to identify phase transitions in a variety of physical
systems. However, there is still a lack of relevant research on non-Bloch energy braiding in …

Exploring topological phases in non-Hermitian systems has attracted significant recent
attention. One intriguing question is how topological edge states compete with the non …

Classification of non-Hermitian topological phases becomes challenging due to interplay of
the band topology and non-Hermiticity. The significant increase in data dimensions and the …