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Machine learning is becoming widely used in analyzing the thermodynamics of many-body condensed matter systems. Restricted Boltzmann machine (RBM) aided Monte Carlo simulations have sparked interest recently, as they manage to speed up classical Monte Carlo simulations. In the talk, based on my recently published paper (Phys. Rev. B 102, 195148), I will explain how we used the convolutional restricted Boltzmann machine (CRBM) method to reduce the number of parameters to be learned drastically by taking advantage of translation invariance. Furthermore, I will show that it is possible to train the CRBM at smaller lattice sizes, and apply it to larger lattice sizes. To demonstrate the efficiency of CRBM, I show the application to the Ising and honeycomb Kitaev models.

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Daniel Alcalde Puente, Ruhr Universität Bochum
Zoom ( URL https://uni-koeln.zoom.us/j/92374710227 )
Contact: Matteo Rizzi