A machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/LightGBM algorithm

A machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/LightGBM algorithm

Item Type Article

Authors Lin, Rongyu;Liu, Zhiyuan;Han, Peng;Lin, Ronghui;Lu, Yi;Cao, Haicheng;Tang, Xiao;Wang, Chuanju;Khandelwal, Vishal;Zhang, Xiangliang;Li, Xiaohang

Citation Lin, R., Liu, Z., Han, P., Lin, R., Lu, Y., Cao, H., Tang, X., Wang, C., Khandelwal, V., Zhang, X., & Li, X. (2022). A machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/

LightGBM algorithm. Journal of Materials Chemistry C. https://

doi.org/10.1039/d2tc02335k Eprint version Publisher's Version/PDF

DOI 10.1039/d2tc02335k

Publisher Royal Society of Chemistry (RSC) Journal Journal of Materials Chemistry C

Rights Archived with thanks to Journal of Materials Chemistry C under a Creative Commons license, details at: http://

creativecommons.org/licenses/by-nc/3.0/

Download date 2023-12-06 19:43:49

Item License http://creativecommons.org/licenses/by-nc/3.0/

Link to Item http://hdl.handle.net/10754/685969

Machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/LightGBM algorithm

Rongyu Lin,^a,† Zhiyuan Liu,^{a, †} Peng Han,^b Ronghui Lin,^a Yi Lu,^a Haicheng Cao,^a Xiao Tang, ^a Chuanju Wang,^a Vishal Khandelwal,^a Xiangliang Zhang,*^b,c and Xiaohang Li *^a

a. Advanced Semiconductor Laboratory, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.

b. Laboratory Machine, Intelligence and Knowledge Engineering (MINE), Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.

c. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

† These authors contributed equally.

*Corresponding author.

* xzhang33@nd.edu and xiaohang.li@kaust.edu.sa

Electronic Supplementary Material (ESI) for Journal of Materials Chemistry C.

This journal is © The Royal Society of Chemistry 2022

S1. Internal quantum efficiency (IQE) predictions from stacked XGBoost/LightGBM model of superlattice (SL) electron blocking layer (EBL) with different composition combinations under the band offset ratio = 0.55.

S2. Internal quantum efficiency (IQE) predictions from stacked XGBoost/LightGBM model of superlattice (SL) electron blocking layer (EBL) with different composition combinations under the band offset ratio = 0.6.

.

S3. Internal quantum efficiency (IQE) predictions from stacked XGBoost/LightGBM model of superlattice (SL) electron blocking layer (EBL) with different composition combinations under the band offset ratio = 0.65.

S4. Internal quantum efficiency (IQE) predictionsfrom stacked XGBoost/LightGBM model of superlattice (SL) electron blocking layer (EBL) with different composition combinations under the band offset ratio = 0.7.