Machine learning phases of matter - CANCELLED

Synopsis

I will discuss how neural networks can be used to identify phases and phase transitions in condensed matter systems via supervised machine learning. We show that a standard feed-forward neural network can be trained to detect multiple types of order parameter directly from raw state configurations sampled with Monte Carlo. In addition, they can detect highly non-trivial states such as Coulomb phases, and if modified to a convolutional neural network, topological phases with no conventional order parameter. We show that this classification occurs within the neural network without knowledge of the Hamiltonian or even the general locality of interactions. Lastly, I will discuss how these ideas can be used to attempt to circumvent the Monte Carlo sign problem in fermionic systems.