Machine Learning Prediction for Bandgaps of Inorganic Materials
Lang Wu, Yue Xiao, Mithun Ghosh, Qiang Zhou and Qing Hao
1 Department of Aerospace and Mechanical Engineering, University of Arizona, Tucson, AZ, 85721, USA
2 Department of Systems and Industrial Engineering, University of Arizona, Tucson, AZ, 85721, USA
Abstract
Machine learning approaches are explored to predict the bandgaps of inorganic compounds using known compositional features, based on a dataset of 3,896 compounds with experimentally measured bandgaps. Particularly among various existing methods, we propose a new method, random forest with Gaussian process model as leaf nodes (RF-GP), and show its advantages. We have also investigated ensemble learning methods, which produce superior results to other traditional machine learning methods, but at the cost of extra computational load and further reduced interpretability.
Publication details
Received: 18 Apr 2020
Revised: 04 Jun 2020
Accepted: 04 Jun 2020
Published online: 04 Jun 2020
Article type:
Research Paper
DOI:
10.30919/esmm5f756
Volume:
9
Page:
34-39
Citation:
ES Materials & Manufacturing, 2020, 9, 34-39
Permissions:
Copyright
Number of downloads:
4697
Citation Information:
12
Description:
A new machine learning method is used to predict the bandgaps of inorganic compounds using known com....
A new machine learning method is used to predict the bandgaps of inorganic compounds using known compositional features.
Supplementary Information:
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