Asset Details
Do you wish to reserve the book?
Machine Learning Approaches to Optimize the Performance of the Novel Stable Lead-free Heterojunction CsGeI3/CsSn(I1−xBrx)3-based Perovskite Solar Cell
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Machine Learning Approaches to Optimize the Performance of the Novel Stable Lead-free Heterojunction CsGeI3/CsSn(I1−xBrx)3-based Perovskite Solar Cell
Do you wish to request the book?
Machine Learning Approaches to Optimize the Performance of the Novel Stable Lead-free Heterojunction CsGeI3/CsSn(I1−xBrx)3-based Perovskite Solar Cell
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Machine Learning Approaches to Optimize the Performance of the Novel Stable Lead-free Heterojunction CsGeI3/CsSn(I1−xBrx)3-based Perovskite Solar Cell
2025
Overview
This work aims to design and predict the performance of a novel heterojunction perovskite solar cell (PSC) based on CsGeI
3
/CsSn(I
1−
x
Br
x
)
3
using machine learning (ML). Electrical parameters including open-circuit voltage (
V
OC
), short-circuit current (
J
SC
), fill factor (FF), and power conversion efficiency (PCE) are generated using SCAPS-1D due to its high accuracy in matching experimental results, and are used to build two ML models based on polynomial regression (PR) and XGBoost (XGB) by considering the thickness
H
1
(CsSn(I
1−
x
Br
x
)
3
) and
H
2
(CsGeI
3
) and the bromine composition (
x
) as parameters. The polynomial degree and maximum depth are considered as parameters for PR and XGB. Performance metrics including training and testing scores, correlation (
r
), and mean squared error (MSE) are calculated to investigate the accuracy, while the cross-validation (CV) scores are used to determine the stability and the overfitting. The results reveal the best performance for
R
2
,
r
,
MSE
, and CV for PR of 0.9969, 0.9915, 0.0245, and 0.9676, respectively, while those for XGB are 0.9990, 0.9991, 0.0005, and 0.9864, respectively. Moreover, the best polynomial degree and maximum depth are 9 and 5, respectively. We also reveal that the dependence of the optoelectronic parameters in terms of bromine composition in CsSn(I
1−
x
Br
x
)
3
creates a powerful internal electrostatic field between the two absorbers for low bromine composition, which favors the extraction of the carriers out of the device. Considering both stability and performance, this prototype can achieve PCE of 29.62%, 29.54%, and 29.61% for actual, PR, and XGB, respectively, when
H
1
,
x
, and
H
2
are 1 μm, 0.6, and 0.2 μm, respectively, compared to 21.91% and 17.61% for existing CsSn(I
1−
x
Br
x
)
3
and CsGeI
3
homojunction.
Publisher
Springer US,Springer Nature B.V
We currently cannot retrieve any items related to this title. Kindly check back at a later time.