  TurboGenius is an open-source Python package designed to fully control ab
initio quantum Monte Carlo (QMC) jobs using a Python script, which allows one
to perform high-throughput calculations combined with TurboRVB [K. Nakano et
al. J. Phys. Chem. 152, 204121 (2020)]. This paper provides an overview of the
TurboGenius package and showcases several results obtained in a high-throughput
mode. For the purpose of performing high-throughput calculations with
TurboGenius, we implemented another open-source Python package, TurboWorkflows,
that enables one to construct simple workflows using TurboGenius. We
demonstrate its effectiveness by performing (1) validations of density
functional theory (DFT) and QMC drivers as implemented in the TurboRVB package
and (2) benchmarks of Diffusion Monte Carlo (DMC) calculations for several data
sets. For (1), we checked inter-package consistencies between TurboRVB and
other established quantum chemistry packages. By doing so, we confirmed that
DFT energies obtained by PySCF are consistent with those obtained by TurboRVB
within the local density approximation (LDA), and that Hartree-Fock (HF)
energies obtained by PySCF and Quantum Package are consistent with variational
Monte Carlo energies obtained by TurboRVB with the HF wavefunctions. These
validation tests constitute a further reliability check of the TurboRVB
package. For (2), we benchmarked atomization energies of the Gaussian-2 set,
binding energies of the S22, A24, and SCAI sets, and equilibrium lattice
parameters of 12 cubic crystals using DMC calculations. We found that, for all
compounds analyzed here, the DMC calculations with the LDA nodal surface give
satisfactory results, i.e., consistent either with high-level computational or
with experimental reference values.
