TurboGenius: Python suite for high-throughput calculations of ab initio quantum Monte Carlo methods

Abstract

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.

arXiv comment44 pages

References in corpus (40)

• Restoring the density-gradient expansion for exchange in solids and surfaces
• Generalized gradient approximation for solids and their surfaces
• AFLOW: An automatic framework for high-throughput materials discovery
• Recent developments in the PySCF program package
• Theoretical and experimental evidence for a post-perovskite phase of MgSiO3 in Earth's D" layer
• A Map of the Inorganic Ternary Metal Nitrides
• AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance
• Optimization of quantum Monte Carlo wave functions by energy minimization
• Green Function Monte Carlo with Stochastic Reconfiguration
• Wave function optimization in the variational Monte Carlo method
• Beyond the locality approximation in the standard diffusion Monte Carlo method
• Geminal wavefunctions with Jastrow correlation: a first application to atoms
• Quantum Package 2.0: An Open-Source Determinant-Driven Suite of Programs
• Zero-Variance Zero-Bias Principle for Observables in quantum Monte Carlo: Application to Forces
• Multi-Determinant Wave-functions in Quantum Monte Carlo
• A New Generation of Effective Core Potentials for Correlated Calculations
• Diffusion Monte Carlo with lattice regularization
• Benchmark of Exchange-Correlation Functionals for High Pressure Hydrogen using Quantum Monte Carlo
• Quantum Monte Carlo applied to solids
• New generation of effective core potentials from correlated calculations: 3d transition metal series
• Approaching Chemical Accuracy with Quantum Monte Carlo
• Benchmark all-electron ab initio quantum Monte Carlo calculations for small molecules
• New generation of effective core potentials from correlated calculations: 2nd row elements
• Quantum phase diagram of high-pressure hydrogen
• TurboRVB: a many-body toolkit for {\it ab initio} electronic simulations by quantum Monte Carlo
• Almost exact energies for the Gaussian-2 set with the semistochastic heat-bath configuration interaction method
• Quantum Monte Carlo for Noncovalent Interactions: An Efficient Protocol Attaining Benchmark Accuracy
• A new generation of effective core potentials from correlated calculations: 4s and 4p main group elements and first row additions
• Benchmarking Hydrogen-Helium Mixtures with QMC: Energetics, Pressures, and Forces
• Correlation-Driven Dimerization and Topological Gap Opening in Isotropically Strained Graphene
• Cohesive energy and structural parameters of binary oxides of groups IIA and IIIB from diffusion quantum Monte Carlo
• TREXIO: A File Format and Library for Quantum Chemistry
• Atomic forces by quantum Monte Carlo: application to phonon dispersion calculation
• All-electron quantum Monte Carlo with Jastrow single determinant Ansatz: application to the sodium dimer
• Towards Quantum Monte Carlo Forces on Heavier Ions: Scaling Properties
• Space-warp coordinate transformation for efficient ionic force calculations in quantum Monte Carlo
• PyQMC: an all-Python real-space quantum Monte Carlo module in PySCF
• Diffusion Monte Carlo Study on Relative Stabilities of Boron Nitride Polymorphs
• Speeding up the ab initio diffusion Monte Carlo by a smart lattice regularization
• Towards chemical accuracy using the Jastrow correlated antisymmetrized geminal power ansatz

Cited by in corpus (8)

• Principal deuterium Hugoniot via Quantum Monte Carlo and $\Delta$-learning
• Systematic discrepancies between reference methods for non-covalent interactions within the S66 dataset
• Toward improved property prediction of 2D materials using many-body quantum Monte Carlo methods
• Basis set incompleteness errors in fixed-node diffusion Monte Carlo calculations on non-covalent interactions
• Efficient calculation of unbiased atomic forces in ab initio Variational Monte Carlo
• Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimer
• Beyond single-reference fixed-node approximation in ab initio Diffusion Monte Carlo using antisymmetrized geminal power applied to systems with hundreds of electrons
• A Denser Hydrogen Inferred from First-Principles Simulations Challenges Jupiter's Interior Models