README.md

metadata

license: other
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - materials-science
  - chemistry
  - foundry-ml
  - scientific-data
size_categories:
  - 1K<n<10K

Foundry - Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning

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

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/wg30-95z0
• Year: 2022
• Authors: Huang, Bing, von Lilienfeld, O., Krogel, Jaron T, Benali, Anouar
• Data Type: tabular

Fields

Field	Role	Description	Units
fragment	input	Fragment ID - to match with full dataset
xyz	input	XYZ formatted string of the molecule structure
pymatgen	input	pymatgen Molecule JSON string of the molecule stru
HF	target	All-electron calculations with cc-pvtz basis set,	Ha
PBE	target	All-electron calculations with cc-pvtz basis set,	Ha
PBE0	target	All-electron calculations with cc-pvtz basis set,	Ha
B3LYP	target	All-electron calculations with cc-pvtz basis set,	Ha
DMC(HF)	target	All-electron calculations with cc-pvtz basis set,	Ha
DMC(PBE)	target	All-electron calculations with cc-pvtz basis set,	Ha
DMC(PBE0)	target	All-electron calculations with cc-pvtz basis set,	Ha
DMC(B3LYP)	target	All-electron calculations with cc-pvtz basis set,	Ha
DMC(HF)_err	target	Error associated with, DMC(HF)	Ha
DMC(PBE)_err	target	Error associated with, DMC(PBE)	Ha
DMC(PBE0)_err	target	Error associated with, DMC(PBE0)	Ha
DMC(B3LYP)_err	target	Error associated with, DMC(B3LYP)	Ha

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/wg30-95z0")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("foundry_qmc_ml_v1.1")

Citation

@misc{https://doi.org/10.18126/wg30-95z0
doi = {10.18126/wg30-95z0}
url = {https://doi.org/10.18126/wg30-95z0}
author = {Huang, Bing and von Lilienfeld, O. and Krogel, Jaron T and Benali, Anouar}
title = {Foundry - Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning}
keywords = {machine learning, foundry, QMC, energy, molecules, chemistry}
publisher = {Materials Data Facility}
year = {root=2022}}

License

other

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This dataset was exported from Foundry-ML, a platform for materials science datasets.