Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

We analyze the zero-temperature phases of an array of neutral atoms on the kagome lattice,
interacting via laser excitation to atomic Rydberg states. Density-matrix renormalization …

Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

The Landau description of phase transitions relies on the identification of a local order
parameter that indicates the onset of a symmetry-breaking phase. In contrast, topological …

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 …

In this Letter we supervisedly train neural networks to distinguish different topological
phases in the context of topological band insulators. After training with Hamiltonians of one …

Neural-network-based machine learning is emerging as a powerful tool for obtaining phase
diagrams when traditional regression schemes using local equilibrium order parameters are …

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 …