foundry-ml/dataset_perovskite_asr

Critical Assessment of Electronic Structure Descriptors for Predicting Perovskite Catalytic Properties

Dataset containing perovskite oxide area specific resistances for 289 materials

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/aw1n-3z96
• Year: 2023
• Authors: Jacobs, Ryan, Liu, Jian, Abernathy, Harry, Morgan, Dane
• Data Type: tabular

Fields

Field	Role	Description	Units
Material composition	input	Material composition
Material composition site	input	Material composition with site groupings
Abbreviated name	input	Material short name
Electrolyte class	input	Electrolyte type used in measurement
Is well studied? (>4 materials)	input	Whether data is averaged over 4 or more independen
ML pred ASR barrier (eV)	input	ML-predicted activation barrier for ASR	eV
Log ASR (Ohm-cm2) at T=773 K	target	Log ASR at 773 K	Ohm-cm2
Log ASR (Ohm-cm2) at T=873 K	target	Log ASR at 873 K	Ohm-cm2
Log ASR (Ohm-cm2) at T=973 K	target	Log ASR at 973 K	Ohm-cm2
Log ASR (Ohm-cm2) at T=1073 K	target	Log ASR at 1073 K	Ohm-cm2

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/aw1n-3z96")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("Dataset_perovskite_ASR")

Citation

@misc{https://doi.org/10.18126/aw1n-3z96
doi = {10.18126/aw1n-3z96}
url = {https://doi.org/10.18126/aw1n-3z96}
author = {Jacobs, Ryan and Liu, Jian and Abernathy, Harry and Morgan, Dane}
title = {Critical Assessment of Electronic Structure Descriptors for Predicting 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.