Datasets:

foundry-ml
/

foundry_mp_band_gaps_v1-1

license: cc-by-4.0
task_categories:
  - tabular-regression
  - tabular-classification
tags:
  - materials-science
  - chemistry
  - foundry-ml
  - scientific-data
size_categories:
  - 1K<n<10K

Graph Network Based Deep Learning of Band Gaps - Materials Project PBE Band Gaps

Dataset Information

Source: Foundry-ML
DOI: 10.18126/vjwr-5bs9
Year: 2021
Authors: Li, Xiang-Guo, Blaiszik, Ben, Schwarting, Marcus, Jacobs, Ryan, Scourtas, Aristana, Schmidt, KJ, Voyles, Paul, Morgan, Dane
Data Type: tabular

Fields

Field	Role	Description	Units
reference	input	source publication of the band gap value
icsd_id	input	corresponding id in ICSD of this compound
structure	input	the structure of this compound
composition	input	reduced composition of this compound
comments	input	Additional information about this bandgap measurem
bandgap type	input	the type of the band gap, e.g., direct or indirect
comp method	input	functional used to calculate the band gap
space group	input	the space group of this compound
bandgap value (eV)	target	value of the band gap	eV

Splits

train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/vjwr-5bs9")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("foundry_mp_band_gaps_v1.1")

Citation

@misc{https://doi.org/10.18126/vjwr-5bs9
doi = {10.18126/vjwr-5bs9}
url = {https://doi.org/10.18126/vjwr-5bs9}
author = {Li, Xiang-Guo and Blaiszik, Ben and Schwarting, Marcus and Jacobs, Ryan and Scourtas, Aristana and Schmidt, KJ and Voyles, Paul and Morgan, Dane}
title = {Graph Network Based Deep Learning of Band Gaps - Materials Project PBE Band Gaps}
keywords = {machine learning, foundry, band gap, neural network}
publisher = {Materials Data Facility}
year = {root=2021}}

License

CC-BY 4.0

This dataset was exported from Foundry-ML, a platform for materials science datasets.