README.md

metadata

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

A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning

Dataset containing experimental solid state Li electrolyte conductivities for 372 materials

Dataset Information

• Source: Foundry-ML
• DOI: 10.18126/k9tn-3965
• Year: 2023
• Authors: Hargreaves, Cameron J., Gaultois, Michael W., Daniels, Luke M., Watts, Emma J., Kurlin, Vitaliy A., Moran, Michael, Dang, Yun, Morris, Rhun, Morscher, Alexandra, Thompson, Kate, Wright, Matthew A., Prasad, Beluvalli-Eshwarappa, Blanc, Frédéric, Collins, Chris M., Crawford, Catriona A., Duff, Benjamin B., Evans, Jae, Gamon, Jacinthe, Han, Guopeng, Leube, Bernhard T., Niu, Hongjun, Perez, Arnaud J., Robinson, Aris, Rogan, Oliver, Sharp, Paul M., Shoko, Elvis, Sonni, Manel, Thomas, William J., Vasylenko, Andrij, Wang, Lu, Rosseinsky, Matthew J., Dyer, Matthew S.
• Data Type: tabular

Fields

Field	Role	Description	Units
ID	input	Entry ID
composition	input	Material composition
source	input	Original data source
temperature	input	Temperature of measured conductivity	degC
target	input	Li conductivity (log scale)	S/cm
family	input	Material structure family
ChemicalFamily	input	Material chemical family
structure	input	Structure group encoding
pretty_formula	input	Material composition reduced form
classification_target	input	Designation of low/high conductivity
loco	input	Cluster number
formula	input	Material composition reduced form
log_target	target	Li conductivity (log scale)	S/cm

Splits

• train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/k9tn-3965")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("Dataset_Li_conductivity")

Citation

@misc{https://doi.org/10.18126/k9tn-3965
doi = {10.18126/k9tn-3965}
url = {https://doi.org/10.18126/k9tn-3965}
author = {Hargreaves, Cameron J. and Gaultois, Michael W. and Daniels, Luke M. and Watts, Emma J. and Kurlin, Vitaliy A. and Moran, Michael and Dang, Yun and Morris, Rhun and Morscher, Alexandra and Thompson, Kate and Wright, Matthew A. and Prasad, Beluvalli-Eshwarappa and Blanc, Frédéric and Collins, Chris M. and Crawford, Catriona A. and Duff, Benjamin B. and Evans, Jae and Gamon, Jacinthe and Han, Guopeng and Leube, Bernhard T. and Niu, Hongjun and Perez, Arnaud J. and Robinson, Aris and Rogan, Oliver and Sharp, Paul M. and Shoko, Elvis and Sonni, Manel and Thomas, William J. and Vasylenko, Andrij and Wang, Lu and Rosseinsky, Matthew J. and Dyer, Matthew S.}
title = {A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning}
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.