Datasetpublic

foundry-ml/wolverton_oxides_v1-1

High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites Dataset containing DFT-calculated formation energy and convex hull energies of 4914 perovskite oxides Dataset Information Source: Foundry-ML DOI: 10.18126/h15n-7wu8 Year: 2022 Authors: Emery, Antoine, Wolverton, Chris Data Type: tabular Fields Field Role Description Units formula input Material composition atom a… See the full description on the dataset page: https://huggingface.co/datasets/foundry-ml/wolverton_oxides_v1-1.

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High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites

Dataset containing DFT-calculated formation energy and convex hull energies of 4914 perovskite oxides

Dataset Information

—Source: Foundry-ML
—DOI: 10.18126/h15n-7wu8
—Year: 2022
—Authors: Emery, Antoine, Wolverton, Chris
—Data Type: tabular

Fields

Field	Role	Description	Units
formula	input	Material composition
atom a	input	Element on the A-site sublattice
atom b	input	Element on the B-site sublattice
lowest distortion	input	Structural category of perovskite structure type
e_form	target	DFT-calculated formation energy, relative to eleme	eV/atom
e_hull	target	DFT-calculated convex hull energy	eV/atom
mu_b	input	Magnetic moment of the relaxed structure	Bohr magneton
vpa	input	Atomic volume of the relaxed structure	Ang^3 per atom
gap pbe	input	DFT-PBE calculated band gap	eV
a	input	Lattice constant of the a-axis	Angstrom
b	input	Lattice constant of the b-axis	Angstrom
c	input	Lattice constant of the c-axis	Angstrom
alpha	input	Lattice alpha angle	degrees
beta	input	Lattice beta angle	degrees
gamma	input	Lattice gamma angle	degrees
e_form oxygen	target	Oxygen vacancy formation energy	eV

Splits

—train: train

Usage

With Foundry-ML (recommended for materials science workflows)

python

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/h15n-7wu8")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

python

from datasets import load_dataset

dataset = load_dataset("wolverton_oxides_v1.1")

Citation

bibtex

@misc{https://doi.org/10.18126/h15n-7wu8
doi = {10.18126/h15n-7wu8}
url = {https://doi.org/10.18126/h15n-7wu8}
author = {Emery, Antoine and Wolverton, Chris}
title = {High-throughput DFT calculations of formation energy, stability and oxygen vacancy formation energy of ABO3 perovskites}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}

License

CC-BY 4.0

This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.