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Bandgap analysis of transition-metal dichalcogenide and oxide via machine learning approach.
- Source :
-
Journal of Physics & Chemistry of Solids . Dec2022, Vol. 171, pN.PAG-N.PAG. 1p. - Publication Year :
- 2022
-
Abstract
- Predicting bandgap is a crucial topic in materials informatics, however, it is still difficult when the available dataset is limited and unbalanced. Here, we applied a machine learning approach to construct a prediction model for transition metal dichalcogenides and oxides. Using an oversampling technique and atomistic feature engineering, we successfully constructed the machine learning model and analyzed the correlation with other physical properties. Furthermore, we also utilized the model to obtain a compressive sensing model based on physical quantities for analytic interpretation and quick prediction. • Machine learning model for bandgap prediction. • Oversampling technique. • Compressive sensing. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TRANSITION metal oxides
*PHYSICAL constants
*PREDICTION models
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 00223697
- Volume :
- 171
- Database :
- Academic Search Index
- Journal :
- Journal of Physics & Chemistry of Solids
- Publication Type :
- Academic Journal
- Accession number :
- 159822428
- Full Text :
- https://doi.org/10.1016/j.jpcs.2022.110973