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foundry-ml/elastic_tensor_v1-1

Charting the complete elastic properties of inorganic crystalline compounds Dataset containing DFT-calculated elastic properties for 1181 materials Dataset Information Source: Foundry-ML DOI: 10.18126/9fg1-528u Year: 2022 Authors: de Jong, Maarten, Chen, Wei, Angsten, Thomas, Jain, Anubhav, Notestine, Randy, Gamst, Anthong, Sluiter, Marcel, Ande, Chaitanya Krishna, van der Zwaag, Sybrand, Plata, Jose J., Toher, Cormac, Curtarolo, Stefano, Ceder, Gerbrand, Persson… See the full description on the dataset page: https://huggingface.co/datasets/foundry-ml/elastic_tensor_v1-1.

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Charting the complete elastic properties of inorganic crystalline compounds

Dataset containing DFT-calculated elastic properties for 1181 materials

Dataset Information

—Source: Foundry-ML
—DOI: 10.18126/9fg1-528u
—Year: 2022
—Authors: de Jong, Maarten, Chen, Wei, Angsten, Thomas, Jain, Anubhav, Notestine, Randy, Gamst, Anthong, Sluiter, Marcel, Ande, Chaitanya Krishna, van der Zwaag, Sybrand, Plata, Jose J., Toher, Cormac, Curtarolo, Stefano, Ceder, Gerbrand, Persson, Kristin A., Asta, Mark
—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
space_group	input	Space group number
volume	input	Volume of relaxed structure	Cubic Angstroms
structure	input	Pymatgen structure representation of material
elastic_anisotropy	target	Description of elastic anisotropy
G_Reuss	target	Shear modulus, lower bound for polycrystal	GPa
G_VRH	target	Average shear modulus	GPa
G_Voigt	target	Shear modulus, upper bound for polycrystal	GPa
K_Reuss	target	Bulk modulus, lower bound for polycrystal	GPa
K_VRH	target	Average bulk modulus	GPa
K_Voigt	target	Bulk modulus, upper bound for polycrystal	GPa
poisson_ratio	target	Describes lateral response to loading
compliance_tensor	target	Tensor, describing elastic behavior	GPa
elastic_tensor	target	Tensor, describing elastic behavior in IEEE-format	GPa
elastictensororiginal	target	Tensor, describing elastic behavior, corresponding	GPa
cif	input	Material structure in CIF format
kpoint_density	N/A	K-point density used in DFT calculation
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("10.18126/9fg1-528u")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

python

from datasets import load_dataset

dataset = load_dataset("elastic_tensor_v1.1")

Citation

bibtex

@misc{https://doi.org/10.18126/9fg1-528u
doi = {10.18126/9fg1-528u}
url = {https://doi.org/10.18126/9fg1-528u}
author = {de Jong, Maarten and Chen, Wei and Angsten, Thomas and Jain, Anubhav and Notestine, Randy and Gamst, Anthong and Sluiter, Marcel and Ande, Chaitanya Krishna and van der Zwaag, Sybrand and Plata, Jose J. and Toher, Cormac and Curtarolo, Stefano and Ceder, Gerbrand and Persson, Kristin A. and Asta, Mark}
title = {Charting the complete elastic properties of inorganic crystalline compounds}
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.