Self-learning with neural networks in determinant quantum Monte Carlo studies of the Holstein model.

Machine learning techniques have recently occupied the focus of many investigators in computational many-body physics. In particular, some practitioners of quantum Monte-Carlo have considered the efficacy of various "Self-Learning" techniques which aim to reduce CPU runtime associated with updates and autocorrelation times. We have used artificial neural networks (NN) within determinant quantum Monte-Carlo to improve the scaling of CPU runtime with typical system parameters. This work focuses on a singleband Holstein Hamiltonian, which models Einstein phonons coupled to on-site electrons. We have implemented both fully connected and convolutional NN and used them to study the metallic and insulating phases of this model. To close, we will assess the generality of this approach to other model systems.