foundry-ml/dataset_perovskite_formatione
Computational screening of perovskite metal oxides for optimal solar light capture Dataset containing 9646 perovskite formation energy data points Dataset Information Source: Foundry-ML DOI: 10.18126/xmh8-d711 Year: 2011 Authors: Castelli, Ivano E., Olsen, Thomas, Datta, Soumendu, Landis, David D., Dahl, Søren, Thygesena, Kristian S., Jacobsen, Karsten W. Data Type: tabular Fields Field Role Description Units formula input Material… See the full description on the dataset page: https://huggingface.co/datasets/foundry-ml/dataset_perovskite_formatione.
Computational screening of perovskite metal oxides for optimal solar light capture
Dataset containing 9646 perovskite formation energy data points
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/xmh8-d711
- Year: 2011
- Authors: Castelli, Ivano E., Olsen, Thomas, Datta, Soumendu, Landis, David D., Dahl, Søren, Thygesena, Kristian S., Jacobsen, Karsten W.
- Data Type: tabular
Fields
Splits
- train: train
Usage
With Foundry-ML (recommended for materials science workflows)
from foundry import Foundry
f = Foundry()
dataset = f.get_dataset("10.18126/xmh8-d711")
X, y = dataset.get_as_dict()['train']With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("Dataset_perovskite_formationE")Citation
@misc{https://doi.org/10.18126/xmh8-d711
doi = {10.18126/xmh8-d711}
url = {https://doi.org/10.18126/xmh8-d711}
author = {Castelli, Ivano E. and Olsen, Thomas and Datta, Soumendu and Landis, David D. and Dahl, Søren and Thygesena, Kristian S. and Jacobsen, Karsten W.}
title = {Computational screening of perovskite metal oxides for optimal solar light capture}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2011}}License
other
This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.
