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Development of a machine learning finite-range nonlocal density functional.

Zehua Chen1, Weitao Yang2

  • 1Department of Chemistry, Duke University, Durham, North Carolina 27708, USA.

The Journal of Chemical Physics
|January 5, 2024
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Summary
This summary is machine-generated.

A new machine learning approach develops universal atom-centered functionals for electronic structure calculations. This method achieves accuracy comparable to double-hybrid functionals but with lower computational cost, offering a novel pathway for functional development.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Quantum Mechanics

Background:

  • Kohn-Sham density functional theory (KS-DFT) is widely used for electronic structure calculations.
  • Increasing accuracy demands necessitate new approximate functionals.
  • Current nonlocal functionals often rely on orbital dependence, increasing computational expense.

Purpose of the Study:

  • To develop a new approach for describing functional nonlocality.
  • To create a universal atom-centered functional using machine learning.
  • To achieve high accuracy in electronic structure calculations with reduced computational cost.

Main Methods:

  • Partitioning total density into atom-centered local densities.
  • Proposing a many-body expansion truncated at one-body contributions.
  • Employing machine learning to develop a universal atom-centered functional fitted to high-level theory data.

Main Results:

  • The new functional, using only density as the basic variable, shows performance comparable to leading double-hybrid functionals.
  • Demonstrated accuracy for reaction energies, barrier heights, and non-covalent interactions.
  • Achieved comparable results with significantly lower computational cost.

Conclusions:

  • The developed machine learning-based functional offers a promising new pathway for nonlocal functional development.
  • This approach provides a computationally efficient alternative for accurate electronic structure calculations.
  • The universality of the atom-centered functional suggests broad applicability across different systems.