Machine Learning in Many-Body Physics

We are interested in applications of machine learning techniques to solving problems of condensed matter physics.

For example, we demonstrated that neural networks can be used to characterise a phase transition driven by hidden and composite order parameters. We considered several different multilayer lattice models, aiming to determine whether a neural network is ‘smart’ enough to reconstruct the correct order parameters, even when they are not obvious from the spin configuration, but they are given, for instance, by a product of spin variables residing on different layers. This work paves the way for using machine learning techniques to identify non-local and exotic order parameters, such as thoseĀ  identifying nematic and smectic phases.

  • W. Rzadkowski, N. Defenu, S. Chiacchiera, A. Trombettoni, G. Bighin
    Detecting hidden and composite orders in layered models via machine learning
    arXiv:1907.05417 (2019)