The Paradox of Global Multireference Diagnostics

Modern computational chemistry methods are a useful tool for modeling many chemical systems, but they are challenged by multireference species (e.g., transition metals). A variety of diagnostics have been formulated to identify such cases. They are typically developed by analyzing multireference characters of small molecules, and many provide an average picture of the entire system. We caution the use of such diagnostics for large systems because large systems may include parts with varying degrees of multireference characters. Specifically, a small but highly multireference component may yield a large error in absolute terms, which may be masked in an average value over the entire molecule. As the calculation of molecular relative energies often concerns errors in absolute terms, such a false sense of safety may be detrimental. A prospective means to tackle this challenge is to use fractional occupation density to identify potentially problematic components in a system and then examine this moiety with higher-level computations on appropriately constructed smaller models.

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Publication Details

Journal
The Journal of Physical Chemistry A
Published
2024-10-31
DOI
https://doi.org/10.1021/acs.jpca.4c06148
Citations
6
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.70

Funders

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article

The Paradox of Global Multireference Diagnostics

Bun Chan
6 citations
The Journal of Physical Chemistry A
Machine Learning in Materials Science
0.70
article

The Paradox of Global Multireference Diagnostics

Bun Chan
article en
6 citations

Abstract

Modern computational chemistry methods are a useful tool for modeling many chemical systems, but they are challenged by multireference species (e.g., transition metals). A variety of diagnostics have been formulated to identify such cases. They are typically developed by analyzing multireference characters of small molecules, and many provide an average picture of the entire system. We caution the use of such diagnostics for large systems because large systems may include parts with varying degrees of multireference characters. Specifically, a small but highly multireference component may yield a large error in absolute terms, which may be masked in an average value over the entire molecule. As the calculation of molecular relative energies often concerns errors in absolute terms, such a false sense of safety may be detrimental. A prospective means to tackle this challenge is to use fractional occupation density to identify potentially problematic components in a system and then examine this moiety with higher-level computations on appropriately constructed smaller models.

The Journal of Physical Chemistry AVol. 128(45)
Nagasaki University (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 29%
Machine Learning in Materials Science
0.70
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