Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …

The study of topological band structures is an active area of research in condensed matter
physics and beyond. Here, we combine recent progress in this field with developments in …

The discovery of topological features of quantum states plays an important role in modern
condensed matter physics and various artificial systems. Due to the absence of local order …

We propose two machine-learning methods based on neural networks, which we
respectively call the phase-classification method and the temperature-identification method …

We demonstrate that a machine learning technique with a simple feedforward neural
network can sensitively detect two successive phase transitions associated with the …

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

Disorder in condensed matter and atomic physics is responsible for a great variety of
fascinating quantum phenomena, which are still challenging for understanding, not to …

The so-called contemporary AI revolution has reached every corner of the social, human
and natural sciences--physics included. In the context of quantum many-body physics, its …

Image classification is a fundamental problem in computer vision, and neural networks
provide an effective solution. With the advancement of quantum technology, quantum neural …

Using the techniques of neural networks (NN), we study the three-dimensional (3D) five-
state ferromagnetic Potts model on the cubic lattice as well as the two-dimensional (2D) …