Using machine learning to predict the band gap of semiconductors

Shuai Yuan; Ken-ichi Nomura

doi:10.1117/12.2668281

20 April 2023 Using machine learning to predict the band gap of semiconductors

Shuai Yuan, Ken-ichi Nomura

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126023D (2023) https://doi.org/10.1117/12.2668281
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

To accelerate the speed of finding new materials and to predict the attributes of materials, Machine Learning has emerged as a very useful tool in recent times. Predicting the band gap of a new material plays a significant role in evaluating the unknown material. To predict the band gap, several regression methods of supervised learning in machine learning were applied, such as linear regression, k-nearest neighbors-regression, random forest regression, support vector machine and decision tree regression. Besides, comparative results illustrated that the predictions of the random forest and the decision tree methods were very good. This enables the possibility to understand thousands of unknown band gaps of non-metals, especially in case of semiconductors.

Citation Download Citation

Shuai Yuan and Ken-ichi Nomura "Using machine learning to predict the band gap of semiconductors", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126023D (20 April 2023); https://doi.org/10.1117/12.2668281

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available