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--- |
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license: other |
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task_categories: |
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- tabular-regression |
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- tabular-classification |
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tags: |
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- materials-science |
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- chemistry |
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- foundry-ml |
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- scientific-data |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Foundry - Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning |
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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. |
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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. |
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Raw data for these calculations are available at https://doi.org/10.18126/hxlp-v732 |
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## Dataset Information |
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- **Source**: [Foundry-ML](https://github.com/MLMI2-CSSI/foundry) |
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- **DOI**: [10.18126/wg30-95z0](https://doi.org/10.18126/wg30-95z0) |
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- **Year**: 2022 |
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- **Authors**: Huang, Bing, von Lilienfeld, O., Krogel, Jaron T, Benali, Anouar |
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- **Data Type**: tabular |
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### Fields |
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| Field | Role | Description | Units | |
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|-------|------|-------------|-------| |
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| fragment | input | Fragment ID - to match with full dataset | | |
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| xyz | input | XYZ formatted string of the molecule structure | | |
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| pymatgen | input | pymatgen Molecule JSON string of the molecule stru | | |
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| HF | target | All-electron calculations with cc-pvtz basis set, | Ha | |
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| PBE | target | All-electron calculations with cc-pvtz basis set, | Ha | |
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| PBE0 | target | All-electron calculations with cc-pvtz basis set, | Ha | |
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| B3LYP | target | All-electron calculations with cc-pvtz basis set, | Ha | |
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| DMC(HF) | target | All-electron calculations with cc-pvtz basis set, | Ha | |
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| DMC(PBE) | target | All-electron calculations with cc-pvtz basis set, | Ha | |
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| DMC(PBE0) | target | All-electron calculations with cc-pvtz basis set, | Ha | |
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| DMC(B3LYP) | target | All-electron calculations with cc-pvtz basis set, | Ha | |
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| DMC(HF)_err | target | Error associated with, DMC(HF) | Ha | |
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| DMC(PBE)_err | target | Error associated with, DMC(PBE) | Ha | |
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| DMC(PBE0)_err | target | Error associated with, DMC(PBE0) | Ha | |
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| DMC(B3LYP)_err | target | Error associated with, DMC(B3LYP) | Ha | |
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### Splits |
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- **train**: train |
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## Usage |
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### With Foundry-ML (recommended for materials science workflows) |
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```python |
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from foundry import Foundry |
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f = Foundry() |
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dataset = f.get_dataset("10.18126/wg30-95z0") |
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X, y = dataset.get_as_dict()['train'] |
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``` |
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### With HuggingFace Datasets |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("foundry_qmc_ml_v1.1") |
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``` |
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## Citation |
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```bibtex |
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@misc{https://doi.org/10.18126/wg30-95z0 |
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doi = {10.18126/wg30-95z0} |
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url = {https://doi.org/10.18126/wg30-95z0} |
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author = {Huang, Bing and von Lilienfeld, O. and Krogel, Jaron T and Benali, Anouar} |
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title = {Foundry - Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning} |
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keywords = {machine learning, foundry, QMC, energy, molecules, chemistry} |
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publisher = {Materials Data Facility} |
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year = {root=2022}} |
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``` |
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## License |
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other |
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--- |
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*This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.* |
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