metadata
license: other
task_categories:
- tabular-regression
- tabular-classification
tags:
- materials-science
- chemistry
- foundry-ml
- scientific-data
size_categories:
- 1K<n<10K
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
This dataset was exported from Foundry-ML, a platform for materials science datasets.