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Density Functional Theory as a Data Science.

Takao Tsuneda1

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Summary
This summary is machine-generated.

Density functional theory (DFT) functionals and physical corrections are reviewed. DFT development prioritizes physical conditions, minimizing parameters, and ensuring physical legitimacy, even from a data science perspective.

Keywords:
Density functional theoryExchange-correlation functionalsMachine learningPhysical corrections

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Area of Science:

  • Computational chemistry
  • Materials science
  • Quantum mechanics

Background:

  • Density functional theory (DFT) is a powerful quantum mechanical method for electronic structure calculations.
  • Development of DFT functionals and physical corrections is crucial for accurate predictions in various scientific fields.
  • Understanding the role of semiempirical parameters and physical meanings is key to advancing DFT.

Purpose of the Study:

  • To review the development of DFT functionals and physical corrections.
  • To analyze their physical meanings and semiempirical parameters from a data science viewpoint.
  • To interpret the sophistication and physical legitimacy of corrected DFT functionals.

Main Methods:

  • Literature review of DFT functional development.
  • Analysis of physical conditions and constraints in functional design.
  • Evaluation of semiempirical parameters and their impact.
  • Data science perspective on DFT model building.

Main Results:

  • DFT exchange-correlation functionals are largely developed under strict physical conditions, minimizing semiempirical parameters.
  • Physical corrections possess clear, functional-independent physical meanings.
  • Corrections require minimal semiempirical parameters, which are dependent on the combined functional.
  • DFT functionals with physical corrections represent physically legitimated target functions.

Conclusions:

  • DFT functionals with physical corrections are highly sophisticated and physically grounded.
  • The development aligns with data science principles, emphasizing physical legitimacy.
  • This approach offers a robust framework for accurate computational modeling.