About this Biochemistry, Genetics and Molecular Biology article
Bandgap Prediction of Two-dimensional Materials Using Machine Learning by Yu Zhang; Wenjing Xu; Guangjie Liu; Zhiyong Zhang; Jinlong Zhu; Meng Li is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.
The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R^2^ >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contras
It is typically read by researchers, students, and practitioners in Biochemistry, Genetics and Molecular Biology.
- Author
- Yu Zhang; Wenjing Xu; Guangjie Liu; Zhiyong Zhang; Jinlong Zhu; Meng Li
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021
- Language
- EN
- Field
- Biochemistry, Genetics and Molecular Biology (Life Sciences)