Datasetpublic

foundry-ml/piezoelectric_tensor_v1-1

A database to enable discovery and design of piezoelectric materials Dataset containing DFT-calculated piezoelectric properties for 941 materials Dataset Information Source: Foundry-ML DOI: 10.18126/p280-xrvg Year: 2022 Authors: de Jong, Maarten, Chen, Wei, Geerlings, Henry, Asta, Mark, Persson, Kristin A. Data Type: tabular Fields Field Role Description Units material_id input Materials Project ID formula input Material composition… See the full description on the dataset page: https://huggingface.co/datasets/foundry-ml/piezoelectric_tensor_v1-1.

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A database to enable discovery and design of piezoelectric materials

Dataset containing DFT-calculated piezoelectric properties for 941 materials

Dataset Information

—Source: Foundry-ML
—DOI: 10.18126/p280-xrvg
—Year: 2022
—Authors: de Jong, Maarten, Chen, Wei, Geerlings, Henry, Asta, Mark, Persson, Kristin A.
—Data Type: tabular

Fields

Field	Role	Description	Units
material_id	input	Materials Project ID
formula	input	Material composition
nsites	input	Number of sites in the unit cell
point_group	input	String denoting the point group of the structure
space_group	input	Space group number
volume	input	Volume of relaxed structure	Cubic Angstroms
structure	input	Pymatgen structure representation of material
eij_max	target	Maximum longitudinal piezoelectric modulus	C/m2
log(eij_max)	target	Log10 of eij_max	C/m2
v_max	target	Crystallographic direction, corresponding to maxim
piezoelectric_tensor	target	Tensor, describing piezoelectric behavior (IEEE-fo	C/m2
cif	input	Material structure in CIF format
meta	input	Summary of material metadata
poscar	input	Material structure in POSCAR format

Splits

—train: train

Usage

With Foundry-ML (recommended for materials science workflows)

python

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/p280-xrvg")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

python

from datasets import load_dataset

dataset = load_dataset("piezoelectric_tensor_v1.1")

Citation

bibtex

@misc{https://doi.org/10.18126/p280-xrvg
doi = {10.18126/p280-xrvg}
url = {https://doi.org/10.18126/p280-xrvg}
author = {de Jong, Maarten and Chen, Wei and Geerlings, Henry and Asta, Mark and Persson, Kristin A.}
title = {A database to enable discovery and design of piezoelectric materials}
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.