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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Efficient Integral-Direct Methods for Self-Consistent Reduced Density Matrix Functional Theory Calculations on

Y Lemke¹, J Kussmann¹, C Ochsenfeld^1,2

¹Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 5-13, D-81377 Munich, Germany.

Journal of Chemical Theory and Computation

|July 12, 2022

Summary

This summary is machine-generated.

Efficient integral-direct methods accelerate reduced density matrix functional theory (RDMFT) for strongly correlated systems. This breakthrough enables RDMFT applications to large molecules, overcoming previous computational limitations.

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Area of Science:

Quantum chemistry
Computational physics
Materials science

Background:

Reduced density matrix functional theory (RDMFT) is crucial for strongly correlated systems.
Current RDMFT methods face computational bottlenecks due to dependence on natural orbitals and two-electron integrals.
Existing resolution-of-the-identity approaches offer limited improvement for large systems.

Purpose of the Study:

To develop and benchmark efficient integral-direct methods for RDMFT.
To integrate these methods into self-consistent energy minimization frameworks.
To enable RDMFT applications for large, chemically relevant molecular systems.

Main Methods:

Derivation and benchmarking of efficient integral-direct methods for RDMFT.
Integration of integral-direct methods into existing self-consistent frameworks.
Development of improved methods for calculating two-electron integral tensor elements and energy derivatives.

Main Results:

Achieved speedups of up to several orders of magnitude in RDMFT calculations.
Significantly reduced memory requirements for RDMFT computations.
Demonstrated successful application to challenging systems like iron(II) porphyrin.

Conclusions:

The developed integral-direct methods overcome major computational barriers in RDMFT.
These advancements make RDMFT a more practical tool for studying large, complex molecules.
The findings pave the way for broader applications of RDMFT in chemistry and physics.