Abstract
Machine learning approaches are explored to predict the bandgaps of inorganic compounds using known compositional features, based on a dataset of 3896 compounds with experimentally measured bandgaps. In particular,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 overother traditional machine learning methods, but at the cost of extra computational load and further reduced interpretability.
Original language | English (US) |
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Pages (from-to) | 34-39 |
Number of pages | 6 |
Journal | ES Materials and Manufacturing |
Volume | 9 |
DOIs | |
State | Published - Sep 2020 |
ASJC Scopus subject areas
- Building and Construction
- Ceramics and Composites
- Metals and Alloys
- Polymers and Plastics
- Applied Mathematics
- Modeling and Simulation
- Numerical Analysis