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Updated: Nov 23, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Massively parallel quantum chemical density matrix renormalization group method.

Jiri Brabec¹, Jan Brandejs^1,2, Karol Kowalski³

¹J. Heyrovský Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, Prague, Czech Republic.

Journal of Computational Chemistry

|December 30, 2020

Summary

This summary is machine-generated.

This study introduces the first supercomputer application for quantum chemical density matrix renormalization group (QC-DMRG) calculations. The developed parallel scheme demonstrates efficient scaling on large molecular systems, enabling complex quantum chemistry simulations.

Keywords:

DMRG MPI massive parallelization quantum chemistry strong correlation

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Last Updated: Nov 23, 2025

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Published on: April 12, 2019

Area of Science:

Computational Chemistry
Quantum Chemistry
High-Performance Computing

Background:

Quantum chemical density matrix renormalization group (QC-DMRG) is a powerful method for strongly correlated systems.
Scaling QC-DMRG to large molecular systems remains a significant computational challenge.

Purpose of the Study:

To adapt and implement QC-DMRG calculations on a supercomputer platform.
To develop and evaluate a novel parallel scheme for enhanced computational efficiency.

Main Methods:

Development of a parallel scheme using an in-house MPI global memory library.
Combination of operator and symmetry sector parallelisms.
Testing on three diverse molecular systems, including the nitrogenase FeMo cofactor cluster.

Main Results:

Successful implementation of QC-DMRG on a supercomputer.
Demonstrated scalability of the parallel approach up to approximately 2000 CPU cores for a large system (113 electrons, 76 orbitals, bond dimension 6000).

Conclusions:

The developed parallel QC-DMRG approach effectively utilizes supercomputing resources.
This work paves the way for tackling larger and more complex quantum chemistry problems.