This review addresses the method of explicit calculations of interatomic exchange
interactions of magnetic materials. This involves exchange mechanisms normally referred to …

In this book, we provide a comprehensive introduction to the most recent advances in the
application of machine learning methods in quantum sciences. We cover the use of deep …

Applications of neural networks to condensed matter physics are becoming popular and
beginning to be well accepted. Obtaining and representing the ground and excited state …

Identifying phase transitions is one of the key challenges in quantum many-body physics.
Recently, machine learning methods have been shown to be an alternative way of localising …

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 …

Abstract Machine-learning driven models have proven to be powerful tools for the
identification of phases of matter. In particular, unsupervised methods hold the promise to …

Experimental quantum simulators have become large and complex enough that discovering
new physics from the huge amount of measurement data can be quite challenging …

Machine learning methods have been recently applied to learning phases of matter and
transitions between them. Of particular interest is the topological phase transition, such as in …

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

Identification of defects in crystalline structures is of vital importance when describing the
plastic behavior of metals. Despite the increasing number of tools available in the literature …