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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

Car-Parrinello density matrix search with a first principles fictitious electron mass method for electronic wave

Xiaosong Li¹, Christopher L Moss, Wenkel Liang

¹Department of Chemistry, University of Washington, Seattle, Washington 98195-1700, USA. li@chem.washington.edu

The Journal of Chemical Physics

|June 25, 2009

Summary

A new fictitious mass scheme accelerates self-consistent-field (SCF) wave function optimization. The Car-Parrinello density matrix search (CP-DMS) method offers linear scaling and faster convergence for quantum systems.

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Area of Science:

Computational Chemistry
Quantum Mechanics
Materials Science

Background:

The Car-Parrinello method is a first-order propagation technique successful in molecular dynamics.
However, its variations struggle with slow convergence in self-consistent-field (SCF) wave function optimization.

Purpose of the Study:

Introduce a novel first-principles fictitious mass scheme for SCF wave function optimization.
Develop an efficient alternative to diagonalization in SCF calculations.
Enhance the convergence speed and scalability of electronic structure calculations.

Main Methods:

Implemented a fictitious mass scheme to assign different masses to density elements.
Utilized the Car-Parrinello scheme as a density matrix search (DMS) method.
Tested the Car-Parrinello density matrix search (CP-DMS) on alanine helical chains and CdSe quantum dots.

Main Results:

The fictitious mass scheme enables very fast SCF convergence.
CP-DMS demonstrates linear scaling with system size for alanine helical chains.
CP-DMS shows smooth and faster convergence for CdSe quantum dots, outperforming diagonalization-based SCF.

Conclusions:

The developed fictitious mass scheme significantly improves SCF convergence speed.
CP-DMS offers a scalable and efficient approach for electronic wave function optimization.
This method shows promise for handling complex quantum systems where traditional methods face challenges.