Machine-Learning Accelerated Calculations of Correlation Functions

We devise two neural network architectures based on the self-attention mechanism and internal neural representations to predict correlation functions in large systems. The neural networks are trained on small system sizes where conventional methods are efficient. We apply our method to pair-pair correlation functions and one-body correlation functions in both translationally-invariant and translationally-breaking Hartree-Fock calculations.