Superconductivty Data

Donated on 10/11/2018

Two file s contain data on 21263 superconductors and their relevant features.

Dataset Characteristics

Multivariate

Subject Area

Physics and Chemistry

Associated Tasks

Regression

Feature Type

Real

# Instances

21263

# Features

81

Dataset Information

Additional Information

There are two files: (1) train.csv contains 81 features extracted from 21263 superconductors along with the critical temperature in the 82nd column, (2) unique_m.csv contains the chemical formula broken up for all the 21263 superconductors from the train.csv file. The last two columns have the critical temperature and chemical formula. The original data comes from http://supercon.nims.go.jp/index_en.html which is public. The goal here is to predict the critical temperature based on the features extracted.

Has Missing Values?

No

Introductory Paper

A data-driven statistical model for predicting the critical temperature of a superconductor

By K. Hamidieh. 2018

Published in Computational materials science

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
number_of_elementsFeatureIntegerno
mean_atomic_massFeatureContinuousno
wtd_mean_atomic_massFeatureContinuousno
gmean_atomic_massFeatureContinuousno
wtd_gmean_atomic_massFeatureContinuousno
entropy_atomic_massFeatureContinuousno
wtd_entropy_atomic_massFeatureContinuousno
range_atomic_massFeatureContinuousno
wtd_range_atomic_massFeatureContinuousno
std_atomic_massFeatureContinuousno

0 to 10 of 82

Additional Variable Information

Please see the relevant paper for the feature explanations.

Dataset Files

FileSize
train.csv22.8 MB
unique_m.csv4.1 MB

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download (7.9 MB)
1 citations
12704 views

Creators

Kam Hamidieh

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy