“Slim” Benchmark Sets for Faster Method Development

The construction of large benchmark sets has accelerated advancement of quantum chemistry methods, especially in density functional theory and lower-cost methods. However, these large benchmark sets can be unsuitable for cutting-edge method development, because research codes developed for fundamentally new approaches are often inefficient and may consequently struggle to handle large molecules. Here, we introduce Slim benchmark sets that are designed to 'summarize' the statistics of larger (in number and size of molecules) counterparts, but have the advantage that molecules are restricted in size (to 5, 16, and 20 atoms) and may therefore be treated by inefficient implementations. Remarkably, our 16 and 20 atom Slim sets effectively summarize reactions involving much larger numbers of atoms. They thereby allow data-driven methodologies to be exploited in the early stages of cutting-edge method development.

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Publication Details

Journal
Journal of Chemical Theory and Computation
Published
2025-06-16
DOI
https://doi.org/10.1021/acs.jctc.5c00512
Citations
6
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.11

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article

“Slim” Benchmark Sets for Faster Method Development

Stefan Vuckovic, Tim Gould
6 citations
Journal of Chemical Theory and Computation
Machine Learning in Materials Science
3.11
article

“Slim” Benchmark Sets for Faster Method Development

Stefan Vuckovic, Tim Gould
article en
6 citations

Abstract

The construction of large benchmark sets has accelerated advancement of quantum chemistry methods, especially in density functional theory and lower-cost methods. However, these large benchmark sets can be unsuitable for cutting-edge method development, because research codes developed for fundamentally new approaches are often inefficient and may consequently struggle to handle large molecules. Here, we introduce Slim benchmark sets that are designed to 'summarize' the statistics of larger (in number and size of molecules) counterparts, but have the advantage that molecules are restricted in size (to 5, 16, and 20 atoms) and may therefore be treated by inefficient implementations. Remarkably, our 16 and 20 atom Slim sets effectively summarize reactions involving much larger numbers of atoms. They thereby allow data-driven methodologies to be exploited in the early stages of cutting-edge method development.

Journal of Chemical Theory and ComputationVol. 21(13)
Griffith University (AU), University of Fribourg (CH)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Australian Research Council
Openalex Percentile: Top 8%
Machine Learning in Materials Science
3.11
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“Slim” Benchmark Sets for Faster Method Development — Stefan Vuckovic, Tim Gould · Journal of Chemical Theory and Computation (2025) | TGRS Research Map | TGRS