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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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PyCDFT: A Python package for constrained density functional theory.

He Ma^1,2, Wennie Wang³, Siyoung Kim³

¹Department of Chemistry, University of Chicago, Chicago, Illinois, USA.

Journal of Computational Chemistry

|June 5, 2020

Summary

This summary is machine-generated.

We introduce PyCDFT, a new Python package for calculating diabatic states using constrained density functional theory (CDFT). This robust tool integrates with existing quantum chemistry codes, enabling accurate electronic coupling computations for molecular systems.

Keywords:

Python charge transfer constrained density functional theory diabatic states electronic coupling

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Area of Science:

Computational Chemistry
Quantum Mechanics
Materials Science

Background:

Constrained Density Functional Theory (CDFT) is crucial for calculating diabatic states.
Accurate computation of diabatic states is essential for understanding electronic processes in molecules and materials.
Existing CDFT implementations can be complex and difficult to interface with standard quantum chemistry software.

Purpose of the Study:

To present PyCDFT, a user-friendly and customizable Python package for CDFT calculations.
To facilitate the computation of diabatic states and electronic couplings.
To provide a flexible tool that interfaces with established Density Functional Theory (DFT) codes.

Main Methods:

Development of an object-oriented Python package, PyCDFT.
Integration of PyCDFT with existing DFT codes (e.g., Qbox) by adding constraint potentials to the Kohn-Sham Hamiltonian.
Performance of single-point self-consistent-field calculations and geometry optimizations.
Benchmarking accuracy by computing electronic coupling between diabatic states for organic molecules.

Main Results:

PyCDFT successfully computes diabatic states using CDFT.
The package allows for both single-point calculations and geometry optimizations.
Demonstrated accurate electronic coupling calculations for organic molecules, showing agreement with existing methods.
Validated PyCDFT as a robust and flexible tool for CDFT.

Conclusions:

PyCDFT offers a powerful and accessible platform for performing CDFT calculations.
The package enhances the ability to study electronic couplings and diabatic states in various chemical systems.
PyCDFT is a valuable addition to the computational chemistry toolkit, promoting further research in electronic processes.