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 …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Over the past decade, topology has emerged as a major branch in broad areas of physics,
from atomic lattices to condensed matter. In particular, topology has received significant …

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

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

We present AI Poincaré, a machine learning algorithm for autodiscovering conserved
quantities using trajectory data from unknown dynamical systems. We test it on five …

Abstract Machine-learning techniques such as artificial neural networks are currently
revolutionizing many technological areas and have also proven successful in quantum …

In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

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 …

Photonic and phononic topological insulators (TIs) offer numerous opportunities for
manipulating light and sound with high efficiency and resiliency. On the other hand, inverse …