[Dataset request] Foundry - Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning

[gpwolfe]

Name

Gregory Wolfe

Email

[email protected]

Dataset name

Foundry Approaching QMC quality energetics using scalable quantum machine learning

Authors

Huang, Bing; von Lilienfeld, O.; Krogel, Jaron T; Benali, Anouar

Publication link

https://doi.org/10.1021/acs.jctc.2c01058

Data link

https://foundry-ml.org/#/datasets/10.18126%2Fwg30-95z0

Additional links

https://www.materialsdatafacility.org/detail/qmc_ml_v1.1

Dataset description

This dataset contains summary inputs and outputs generated for the Paper "Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning" By B. Huang, O. Anatole von Lilienfeld, J. T. Krogel and A. Benali. Included in the dataset are energies for 1175 molecules calculated with varying methods, associated error calculations, and molecular structures in XYZ and pymatgen Molecule formats. Raw data for these calculations are available at https://doi.org/10.18126/hxlp-v732. Methods include a variety of cross-correlation functionals at the DFT level of theory.

File details

Raw data files in h5 and other pre-final formats available at the materialsdatafacility link. Processed data available from the Foundry link in CSV format -- symbols and coords listed in one string cell
1175 configurations

Method

DFT

Method (other)

No response

Software

None

Software (other)

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Software version(s)

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Additional details

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Property types

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Other/additional property

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Property details

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Elements

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Number of Configurations

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Naming convention

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Configuration sets

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Configuration labels

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Distribution license

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Permissions

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