Force and stress calculations with a neural-network wave function for solids

Accurate ab initio calculations of real solids are of fundamental importance in fields such as chemistry, phases and materials science. Recently, variational Monte Carlo (VMC) based on neural-network wave functions has been developed as a promising option to solve the existing challenges in ab initio calculations. In this study, we discuss the calculation of interatomic forces and stress tensors of real solids with a neural-network-based VMC method. A new scheme for computing forces is proposed based on the space-warp coordination transformation method, which achieves better accuracy, efficiency and robustness than existing methods. In addition, we also designed new periodic features of the neural network to further improve the robustness of force calculations for different lattices. This work paves the way for further extending the application of machine-learning quantum Monte Carlo methods in materials modelling.