QMCTorch: a PyTorch Implementation of Real-Space Quantum Monte Carlo Simulations of Molecular Systems

Abstract

Quantum Monte-Carlo (QMC) simulations allow to compute the electronic structure of quantum systems with high accuracy and can be parallelized over large compute resources. QMC relies on the variational principle and optimize a wave function ansatz to minimize the total energy of the quantum system. QMCTorch expresses this optimization process as a machine learning problem where the wave function ansatz is encoded in a physically-motivated neural network. The use of PyTorch as a backend to perform the optimization, allows leveraging automatic differentiation and GPU computing to accelerate the development and deployment of QMC simulations. QMCTorch supports the use of both Gaussian and Slater type orbitals via interface to popular quantum chemistry packages pyscf and ADF.