foundry-ml/perovskite_stability_v1-1

Predicting the thermodynamic stability of perovskite oxides using machine learning models

Dataset containing DFT-calculated stabilities (as convex hull energies) of 1929 perovskite oxides

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/qe5y-2dnz
• Year: 2022
• Authors: Li, Wei, Jacobs, Ryan, Morgan, Dane
• Data Type: tabular

Fields

Field	Role	Description	Units
Material Composition	input	Perovskite composition
EnergyAboveHull	target	DFT-calculated convex hull energy as meV/atom	meV/atom
Formation_energy	target	DFT-calculated formation energy relative to pure e	eV/atom

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/qe5y-2dnz")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("perovskite_stability_v1.1")

Citation

@misc{https://doi.org/10.18126/qe5y-2dnz
doi = {10.18126/qe5y-2dnz}
url = {https://doi.org/10.18126/qe5y-2dnz}
author = {Li, Wei and Jacobs, Ryan and Morgan, Dane}
title = {Predicting the thermodynamic stability of perovskite oxides using machine learning models}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}

License

CC-BY 4.0

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