Interoperable workflows by exchanging grid-based data between quantum-chemical program packages

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.