“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.
Authors
- Stefan Vuckovic (ORCID: https://orcid.org/0000-0002-0768-9176)
- Tim Gould (ORCID: https://orcid.org/0000-0002-7191-9124)
Institutions
- Griffith University (AU)
- University of Fribourg (CH)
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
Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- Australian Research Council