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

Kousuke Nakano1,2, Oto Kohulák1,3, Abhishek Raghav1,4

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TurboGenius is an open-source Python package for controlling ab initio quantum Monte Carlo (QMC) jobs, enabling high-throughput calculations. Validations and benchmarks confirm its reliability and accuracy for various computational chemistry tasks.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Materials Science

Background:

  • Ab initio quantum Monte Carlo (QMC) methods are crucial for accurate electronic structure calculations.
  • High-throughput computational approaches accelerate scientific discovery.
  • Controlling complex QMC jobs requires specialized software tools.

Purpose of the Study:

  • To introduce and demonstrate the capabilities of the open-source TurboGenius Python package.
  • To facilitate high-throughput ab initio QMC calculations.
  • To validate and benchmark the TurboRVB package through integrated workflows.

Main Methods:

  • Development of the TurboGenius Python package for QMC job control.
  • Implementation of the TurboWorkflows package for constructing calculation workflows.
  • Validation of Density Functional Theory (DFT) and QMC drivers against established quantum chemistry packages (PySCF, Quantum Package).
  • Benchmarking of Diffusion Monte Carlo (DMC) calculations on standard datasets (G2, S22, A24, SCAI, cubic crystals).

Main Results:

  • TurboGenius enables seamless high-throughput QMC calculations.
  • DFT and Hartree-Fock energies computed with TurboRVB show excellent agreement with PySCF and Quantum Package.
  • DMC calculations using the LDA nodal surface provide satisfactory atomization energies, binding energies, and lattice parameters, consistent with reference data.

Conclusions:

  • The TurboGenius and TurboWorkflows packages offer a robust platform for automated, high-throughput QMC studies.
  • Validation confirms the reliability of TurboRVB's DFT and QMC implementations.
  • DMC calculations with the LDA nodal surface are accurate for predicting molecular and solid-state properties.