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.
The magnetization dynamics in nanostructures has been extensively studied in the last decades, and nanomagnetism has evolved significantly over that time, discovering new effects, developing numerous applications, and identifying promising new directions. This includes magnonics, an emerging research field oriented on the study of spin-wave dynamics and their applications. In this context, thin ferromagnetic films with perpendicular magnetic anisotropy (PMA) offer interesting opportunities to study spin waves, in particular, due to out-of-plane magnetization in remanence or at relatively weak external magnetic fields. This is the only magnetization configuration offering isotropic in-plane spin-wave propagation within the sample plane, the forward volume magnetostatic spin-wave geometry. The isotropic dispersion relation is highly important in designing signal-processing devices, offering superior prospects for direct replicating various concepts from photonics into magnonics. Analogous to photonic or phononic crystals, which are the building blocks of optoelectronics and phononics, magnonic crystals are considered as key components in magnonics applications. Arrays of nanodots and structured ferromagnetic thin films with a periodic array of holes, popularly known as antidot lattices based on PMA multilayers have been recently studied. Novel magnonic properties related to propagating spin-wave modes, exploitation of the band gaps, and confined modes, were demonstrated. Also, the existence of nontrivial magnonic band topologies has been shown. Moreover, the combination of PMA and Dzyaloshinskii-Moriya interaction leads to the formation of chiral magnetization states, including N\'eel domain walls, skyrmions, and skyrmionium states.
