Published November 14, 2023
| Version v1
Dataset
Open
COMP6v2 Release
Creators
-
1.
University of Florida
-
2.
Carnegie Mellon University
-
3.
Nvidia (United States)
-
4.
Los Alamos National Laboratory
Description
COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0
The COMP6v2 data is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6
COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl.
It is available at the following levels of theory:
- wB97X/631Gd (data used to train model in the ANI-2x paper)
- wB97MD3BJ/def2TZVPP
- wB97MV/def2TZVPP
- B973c/def2mTZVP
You will notice that COMP6v1 is split into subsets:
- ANI-MD
- DrugBank
- GDB07to09
- GDB10to13
- Tripeptides
- s66x8
As we are releasing multiple levels of theory here, each h5 file is a combination of all subsets.
The sample data loader script provides an example of how to access the contents of the data sets using h5py.
View the supplementary information for more information on the formatting and contents of the data sets.
Details on the generation of this data can be found in the ANI-2x paper.
Files
supplementary_information.pdf
Files
(538.5 MB)
| Name | Size | Download all |
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md5:90c4167b244cfb03fc0fdbf8ff8f3b64
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138.9 MB | Download |
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md5:c3cdcbe2ef396bd669586d171967093b
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183.8 MB | Download |
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md5:2e5f716c490fb0ad127be5cf0d413bf6
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48.9 MB | Download |
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md5:0a417148966022e72f54c135d8f3d4e7
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166.9 MB | Download |
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md5:93065af62e2e0ebe634f74a0c34d231a
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664 Bytes | Download |
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md5:fd6312a019b70f554374a3bd1271842e
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72.9 kB | Preview Download |
Additional details
Related works
- References
- Journal: 10.1021/acs.jctc.0c00121 (DOI)
Dates
- Available
-
2023-11-14
References
- Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens Christian Devereux, Justin S. Smith, Kate K. Huddleston, Kipton Barros, Roman Zubatyuk, Olexandr Isayev, and Adrian E. Roitberg Journal of Chemical Theory and Computation 2020 16 (7), 4192-4202 DOI: 10.1021/acs.jctc.0c00121