  Deep-Learning-based Variational Monte Carlo (DL-VMC) has recently emerged as
a highly accurate approach for finding approximate solutions to the
many-electron Schr\"odinger equation. Despite its favorable scaling with the
number of electrons, $\mathcal{O}(n_\text{el}^{4})$, the practical value of
DL-VMC is limited by the high cost of optimizing the neural network weights for
every system studied. To mitigate this problem, recent research has proposed
optimizing a single neural network across multiple systems, reducing the cost
per system. Here we extend this approach to solids, where similar but distinct
calculations using different geometries, boundary conditions, and supercell
sizes are often required. We show how to optimize a single ansatz across all of
these variations, reducing the required number of optimization steps by an
order of magnitude. Furthermore, we exploit the transfer capabilities of a
pre-trained network. We successfully transfer a network, pre-trained on 2x2x2
supercells of LiH, to 3x3x3 supercells. This reduces the number of optimization
steps required to simulate the large system by a factor of 50 compared to
previous work.
