foundry-ml/dataset_perovskite_tec

Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation

Dataset containing 137 perovskite thermal expansion coefficients from experiment

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/avvr-p174
• Year: 2023
• Authors: McGuinness, Kevin P., Oliynyk, Anton O., Lee, Sangjoon, Molero-Sanchez, Beatriz, Addo, Paul Kwesi
• Data Type: tabular

Fields

Field	Role	Description	Units
Composition	input	Material composition with sites
Initial Temperature (degC)	input	Initial temperature	degC
Final Temperature (degC)	input	Final temperature	degC
TEC (x10^-6 K^-1)	target	Thermal expansion coefficient	K^-1

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/avvr-p174")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("Dataset_perovskite_tec")

Citation

@misc{https://doi.org/10.18126/avvr-p174
doi = {10.18126/avvr-p174}
url = {https://doi.org/10.18126/avvr-p174}
author = {McGuinness, Kevin P. and Oliynyk, Anton O. and Lee, Sangjoon and Molero-Sanchez, Beatriz and Addo, Paul Kwesi}
title = {Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation}
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.