  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 mesoscopic and macroscopic properties. CMP
overlaps with many other important branches of science, such as Chemistry,
Materials Science, Statistical Physics, and High-Performance Computing. With
the advancements in modern Machine Learning (ML) technology, a keen interest in
applying these algorithms to further CMP research has created a compelling new
area of research at the intersection of both fields. In this review, we aim to
explore the main areas within CMP, which have successfully applied ML
techniques to further research, such as the description and use of ML schemes
for potential energy surfaces, the characterization of topological phases of
matter in lattice systems, the prediction of phase transitions in off-lattice
and atomistic simulations, the interpretation of ML theories with
physics-inspired frameworks and the enhancement of simulation methods with ML
algorithms. We also discuss in detail the main challenges and drawbacks of
using ML methods on CMP problems, as well as some perspectives for future
developments.
