Deep-learning-based variational Monte Carlo has emerged as a highly
accurate method for solving the many-electron Schrödinger
equation. Despite favorable scaling with the number of electrons, 𝒪𝒪𝒪nel
4),
the practical value of deep-learning-based variational Monte Carlo is limited
by the high cost of optimizing the neural network weights for every system
studied. Recent research has proposed optimizing a single neural network
across multiple systems, reducing the cost per system. Here we extend this
approach to solids, which require numerous calculations across different
geometries, boundary conditions and supercell sizes. We demonstrate that
optimization of a single ansatz across these variations significantly reduces
optimization steps. Furthermore, we successfully transfer a network trained
on 2 × 2 × 2 supercells of LiH, to 3 × 3 × 3 supercells, reducing the number of
optimization steps required to simulate the large system by a factor of 50
compared with previous work.