foundry-ml/double_perovskite_bandgap_v1-1
Machine learning bandgaps of double perovskites Dataset containing DFT-calculated band gaps of 1306 double perovskite oxide materials Dataset Information Source: Foundry-ML DOI: 10.18126/lss6-o5x4 Year: 2022 Authors: Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., Lookman, T. Data Type: tabular Fields Field Role Description Units formula input Material composition a_1 input Element 1 on the A… See the full description on the dataset page: https://huggingface.co/datasets/foundry-ml/double_perovskite_bandgap_v1-1.
Machine learning bandgaps of double perovskites
Dataset containing DFT-calculated band gaps of 1306 double perovskite oxide materials
Dataset Information
- Source: Foundry-ML
- DOI: 10.18126/lss6-o5x4
- Year: 2022
- Authors: Pilania, G., Mannodi-Kanakkithodi, A., Uberuaga, B. P., Ramprasad, R., Gubernatis, J. E., Lookman, T.
- 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/lss6-o5x4")
X, y = dataset.get_as_dict()['train']With HuggingFace Datasets
from datasets import load_dataset
dataset = load_dataset("double_perovskite_bandgap_v1.1")Citation
@misc{https://doi.org/10.18126/lss6-o5x4
doi = {10.18126/lss6-o5x4}
url = {https://doi.org/10.18126/lss6-o5x4}
author = {Pilania, G. and Mannodi-Kanakkithodi, A. and Uberuaga, B. P. and Ramprasad, R. and Gubernatis, J. E. and Lookman, T.}
title = {Machine learning bandgaps of double perovskites}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}License
CC-BY 4.0
This dataset was exported from [Foundry-ML](https://github.com/MLMI2-CSSI/foundry), a platform for materials science datasets.