With the advent of the 21st century, the demand for computing power and numerical models has led to an integrative approach of using Machine Learning methods in physical sciences, specifically in Statistical Physics. The paper outlines the foundational principles of Statistical Physics and Machine Learning, bridging the gap between these disciplines. It showcases how Machine Learning algorithms have been used to solve complex problems in Solid State Physics, such as predicting phase transitions, simulating frustrating systems, and understanding material properties. The paper also underlines the challenges and limitations encountered in this interdisciplinary venture. The use of Machine Learning models like Variational autoencoder, Restricted Boltzmann Machine, and Reinforcement Learning are examined.
Complex quasi-particles formed by bound electron-hole pairs in semiconductors, also known as excitons, play a fundamental role in their light absorption and emission processes. Recent advances in fabrication and exfoliation techniques enabled the systematic production of few layers two-dimensional (2D) semiconductors, such as transition metal dichalcogenides and black phosphorus, where exciton binding energies are remarkably strong, which results not only in clear excitonic features in absorption and photoluminescence spectra, but also in robust excitons that are controllable e.g. by external electric and magnetic fields. Here, we provide an overview of the recent developments in opto-electronics of 2D semiconductors and discuss them in light of the current theoretical models for excitons.
