Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach / Muhammad Asyraf Janai
This research aims to increase the efficiency of blue phosphorescent light-emitting diode (PhOLED) through machine learning models. Historical data from papers published prior to this research are used to train such model. From the model built, we are able to predict the current efficiency of PhOLED...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Published: |
2019
|
| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/12036/1/Muhammad_Asyraf.pdf http://studentsrepo.um.edu.my/12036/2/Muhammad_Asyraf.pdf http://studentsrepo.um.edu.my/12036/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This research aims to increase the efficiency of blue phosphorescent light-emitting diode (PhOLED) through machine learning models. Historical data from papers published prior to this research are used to train such model. From the model built, we are able to predict the current efficiency of PhOLED from a combination of materials parameters used in a device. Furthermore, the result of this research allows us to quantify the parameter of devices and rank them according to the feature importance. The feature importance describes the impact of any single parameter in a device based on the model and how it affects the device efficiency. The result of our experiment shows that Random Forest, a machine learning algorithm, produces the best fit to our dataset and hence able to make the most accurate prediction of device efficiency. This algorithm is then used to study the complex relationship of device features and efficiencies. It is found from the algorithm that triplet energy of electron transport layer is the most important feature in determining device efficiency among other features.
|
|---|