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Kevin Focke1, Matteo De Santis2, Mario Wolter1

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This summary is machine-generated.

This study introduces PyEmbed, a Python tool simplifying complex quantum chemistry workflows by exchanging electron densities and embedding potentials as grid-based data. This enhances interoperability between different quantum chemistry program packages.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Materials Science

Background:

  • Quantum-chemical subsystem and embedding methods involve intricate workflows.
  • These workflows often require multiple quantum-chemical program packages.
  • Exchanging large datasets beyond basic molecular structures and energies presents interoperability challenges.

Purpose of the Study:

  • To address the interoperability challenge in quantum-chemical subsystem and embedding methods.
  • To develop a method for exchanging electron densities and embedding potentials as grid-based data.
  • To facilitate the development of advanced quantum-chemical methods through modular software.

Main Methods:

  • Implementation of a dedicated code, PyEmbed, within a Python scripting framework.
  • Exchange of electron densities and embedding potentials as grid-based data.
  • Development of modular software libraries to enhance interoperability.

Main Results:

  • PyEmbed facilitates the development of quantum-chemical subsystem and embedding methods.
  • Enabled applications include WFT-in-DFT embedding, relativistic/non-relativistic mixing, real-time TD-DFT-in-DFT, and density-based many-body expansion.
  • Demonstrated the effectiveness of exchanging complex grid-based data.

Conclusions:

  • Exchanging grid-based data is a viable approach for quantum chemistry interoperability.
  • Modular software development, supported by interoperability libraries, is crucial for advancing quantum chemistry.
  • PyEmbed enhances the development and application of sophisticated quantum chemical methods.