2023
Zhao, Qiyuan; Vaddadi, Sai Mahit; Woulfe, Michael; Ogunfowora, Lawal A.; Garimella, Sanjay S.; Isayev, Olexandr; Savoie, Brett M.
Comprehensive exploration of graphically defined reaction spaces Journal Article
In: Sci Data, vol. 10, pp. 145 , 2023.
Abstract | Links | BibTeX | Tags: dataset, Organic reactions
@article{Zhao2023,
title = {Comprehensive exploration of graphically defined reaction spaces},
author = {Qiyuan Zhao and Sai Mahit Vaddadi and Michael Woulfe and Lawal A. Ogunfowora and Sanjay S. Garimella and Olexandr Isayev and Brett M. Savoie},
doi = {10.1038/s41597-023-02043-z},
year = {2023},
date = {2023-03-15},
urldate = {2023-03-15},
journal = {Sci Data},
volume = {10},
pages = {145 },
publisher = {Springer Science and Business Media LLC},
abstract = {Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.},
keywords = {dataset, Organic reactions},
pubstate = {published},
tppubtype = {article}
}
Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.
2020
Smith, Justin S.; Zubatyuk, Roman; Nebgen, Benjamin; Lubbers, Nicholas; Barros, Kipton; Roitberg, Adrian E.; Isayev, Olexandr; Tretiak, Sergei
The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules Journal Article
In: Sci Data, vol. 7, no. 1, 2020, ISSN: 2052-4463.
Abstract | Links | BibTeX | Tags: ANI, dataset, Machine learning potential
@article{Smith2020,
title = {The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules},
author = {Justin S. Smith and Roman Zubatyuk and Benjamin Nebgen and Nicholas Lubbers and Kipton Barros and Adrian E. Roitberg and Olexandr Isayev and Sergei Tretiak},
doi = {10.1038/s41597-020-0473-z},
issn = {2052-4463},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Sci Data},
volume = {7},
number = {1},
publisher = {Springer Science and Business Media LLC},
abstract = {Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.},
keywords = {ANI, dataset, Machine learning potential},
pubstate = {published},
tppubtype = {article}
}
Maximum diversification of data is a central theme in building generalized and accurate machine learning (ML) models. In chemistry, ML has been used to develop models for predicting molecular properties, for example quantum mechanics (QM) calculated potential energy surfaces and atomic charge models. The ANI-1x and ANI-1ccx ML-based general-purpose potentials for organic molecules were developed through active learning; an automated data diversification process. Here, we describe the ANI-1x and ANI-1ccx data sets. To demonstrate data diversity, we visualize it with a dimensionality reduction scheme, and contrast against existing data sets. The ANI-1x data set contains multiple QM properties from 5 M density functional theory calculations, while the ANI-1ccx data set contains 500 k data points obtained with an accurate CCSD(T)/CBS extrapolation. Approximately 14 million CPU core-hours were expended to generate this data. Multiple QM calculated properties for the chemical elements C, H, N, and O are provided: energies, atomic forces, multipole moments, atomic charges, etc. We provide this data to the community to aid research and development of ML models for chemistry.