Machine Learning-Assisted Optimization of Process Parameters for Perovskite Solar Cell Fabrication

Fig. 2. Scatter plot of perovskite solar cell efficiency prediction by different models. (a) LR; (b) SVR; (c) RF; (d) XGBoost; (e) Adaboost; (f) PCEPM

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Fig. 3. Feature importance analysis. (a) Feature importance scatter; (b) feature importance ranking

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Fig. 4. Comparison of experimental and predicted photovoltaic conversion efficiency of perovskite solar cell devices under different experimental conditions. (a) Structure of perovskite solar cell devices; (b) power conversion efficiency under different laboratory conditions

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Fig. 5. Performance testing. (a) IV testing; (b) EQE testing; (c) XRD testing; (d) PL testing; (e) UV-Vis testing

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Fig. 6. SEM maps at different magnifications. (a) 1 μm; (b) 500 nm; (c) 200 nm; (d) 100 nm

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Table 1. Process parameters in the preparation of perovskite solar cells

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Table 1. Process parameters in the preparation of perovskite solar cells

Number	Feature name	Parameter space
1	NMP （N-Methylpyrrolidone） /（μL/mL）	$\{0, 30\}$
2	CY （Cyrene） /（μL/mL）	$\{0, 30\}$
3	THF （2-Methyltetrahydrofuran） /（μL/mL）	$\{0, 10, 15, 20, 26, 28, 30, 32\}$
4	CHP （N-Cyclohexyl-2-pyrrolidone） /（μL/mL）	$\{0, 30\}$
5	DMSO （Dimethyl sulfoxide） /（μL/mL）	$\{47, 63, 84, 93, 115, 125, 140, 165, 180, 200, 215\}$
6	TMSO（Tetramethylene sulfoxide） /（μL/mL）	$\{0, 15, 31, 57, 65, 78, 93\}$
7	DMF（N，N-Dimethylformamide） /（μL/mL）	$\{830, 865, 890, 905\}$
8	RbI （Rubidium iodide）∶CsI （Cesium iodide）	$\{0.889, 0.911, 0.956, 1\}$
9	Purification	$\{P E A I C l, P M A 4\}$
10	Ammonium ion /（μL/mL）	$\{0, 1, 2, 3\}$
11	Annealing method	$\{C o n v e n t i o n a l, s o l v e n t a t m o s p h e r e\}$
12	Annealing time ratio	$\{0.167, 0.333, 0.5, 1\}$
13	Light intensity /lx	$[0, 110]$

Table 2. Comparison of prediction performance of different models on the training and testing datasets

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Table 2. Comparison of prediction performance of different models on the training and testing datasets

Model	Training dataset			Testing dataset
Model	RMSE	MAE	$R^{2}$	RMSE	MAE	$R^{2}$
LR	1.158	0.729	0.413	0.993	0.764	0.585
SVR-RBF	1.079	0.564	0.490	0.788	0.552	0.738
RF	0.498	0.247	0.891	0.636	0.478	0.830
XGBoost	0.166	0.126	0.988	0.652	0.481	0.821
AdaBoost	0.227	0.201	0.977	0.669	0.505	0.812
PCEPM	0.250	0.188	0.973	0.620	0.469	0.838

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Yin Gao, Yang Li, Chuanglin Xia, Qian Chen. Machine Learning-Assisted Optimization of Process Parameters for Perovskite Solar Cell Fabrication[J]. Laser & Optoelectronics Progress, 2025, 62(9): 0916003

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Paper Information

Category: Materials

Received: Dec. 20, 2024

Accepted: Feb. 7, 2025

Published Online: May. 9, 2025

The Author Email: Yang Li (insidesun51@163.com)

DOI:10.3788/LOP242462

CSTR:32186.14.LOP242462

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology