Datasets:

foundry-ml
/

dielectric_constant_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

High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials

Dataset containing DFT-calculated dielectric properties for 1056 materials

Dataset Information

Source: Foundry-ML
DOI: 10.18126/racd-go9m
Year: 2022
Authors: Petousis, Ioannis, Mrdjenovich, David, Ballouz, Eric, Liu, Miao, Winston, Donald, Chen, Wei, Graf, Tanja, Schladt, Thomas D., Persson, Kristin A., Prinz, Fritz B.
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
band_gap	input	Bandgap of material from Materials Project	eV
e_electronic	target	Electronic portion of the dielectric constant tens
e_total	target	Total dielectic constant tensor
n	target	Index of refraction
poly_electronic	target	Polycrystal estimate of electronic part of dielect
poly_total	target	Polycrystal estimate of total dielectric constant
log(poly_total)	target	log10 of poly total
pot_ferroelectric	target	Whether the material is potentially a ferroelectri
cif	input	Material structure in CIF format
meta	input	DFT calculation metadata
poscar	input	Material structure in POSCAR format

Splits

train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

f = Foundry()
dataset = f.get_dataset("10.18126/racd-go9m")
X, y = dataset.get_as_dict()['train']

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("dielectric_constant_v1.1")

Citation

@misc{https://doi.org/10.18126/racd-go9m
doi = {10.18126/racd-go9m}
url = {https://doi.org/10.18126/racd-go9m}
author = {Petousis, Ioannis and Mrdjenovich, David and Ballouz, Eric and Liu, Miao and Winston, Donald and Chen, Wei and Graf, Tanja and Schladt, Thomas D. and Persson, Kristin A. and Prinz, Fritz B.}
title = {High-throughput screening of inorganic compounds for the discovery of novel dielectric and optical materials}
keywords = {machine learning, foundry}
publisher = {Materials Data Facility}
year = {root=2022}}

License

CC-BY 4.0

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