Datasets:

foundry-ml
/

superconductivity_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

Machine learning modeling of superconducting critical temperature

Dataset containing experimentally measured superconducting critical temperatures for 16414 materials

Dataset Information

Source: Foundry-ML
DOI: 10.18126/xlfr-hjrn
Year: 2022
Authors: Stanev, Valentin, Oses, Corey, Kusne, A. Gilad, Rodriguez, Efrain, Paglione, Johnpierre, Curtarolo, Stefano, Takeuchi, Ichiro
Data Type: tabular

Fields

Field	Role	Description	Units
name	input	Material composition
Tc	target	Experimental superconducting critical temperature	K

Splits

train: train

Usage

With Foundry-ML (recommended for materials science workflows)

from foundry import Foundry

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

With HuggingFace Datasets

from datasets import load_dataset

dataset = load_dataset("superconductivity_v1.1")

Citation

@misc{https://doi.org/10.18126/xlfr-hjrn
doi = {10.18126/xlfr-hjrn}
url = {https://doi.org/10.18126/xlfr-hjrn}
author = {Stanev, Valentin and Oses, Corey and Kusne, A. Gilad and Rodriguez, Efrain and Paglione, Johnpierre and Curtarolo, Stefano and Takeuchi, Ichiro}
title = {Machine learning modeling of superconducting critical temperature}
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.