foundry-ml/dataset_perovskite_stability_updated

Machine Learning Design of Perovskite Catalytic Properties

Dataset containing 2844 perovskite stability data points from DFT

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/xcye-zy28
• Year: 2023
• Authors: Jacobs, Ryan, Liu, Jian, Abernathy, Harry, Morgan, Dane
• Data Type: tabular

Fields

Field	Role	Description	Units
composition	input	Material composition with sites
composition (no brackets)	input	Material composition
O pband (eV)	input	DFT-calculated O p-band center	eV
energy	input	DFT-calculated total energy	eV/cell
Nominal d #	input	Number of transition metal d electrons
Band gap (eV)	input	DFT-calculated band gap	eV
E_hull (meV/atom)	target	Energy above hull at UHV, 1200 K	meV/atom
E_above_hull_closed (meV/atom)	target	Energy above hull of closed system	meV/atom
E_above_hull_open (meV/atom)	target	Energy above hull of open system at 500 C, room ai	meV/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/xcye-zy28")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("Dataset_perovskite_stability_updated")

Citation

@misc{https://doi.org/10.18126/xcye-zy28
doi = {10.18126/xcye-zy28}
url = {https://doi.org/10.18126/xcye-zy28}
author = {Jacobs, Ryan and Liu, Jian and Abernathy, Harry and Morgan, Dane}
title = {Machine Learning Design of Perovskite Catalytic Properties}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2023}}

License

other

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