Min Lei
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Min Lei has a total of 18 co-authors. Here are the top co-authors by number of shared publications:
- Di Huang
Hunan University of Technology, Zhuzhou, 412008, CHINA
2 shared publications - Kuo Wang
College of Transportation and Electrical Engineering Department of Physics and RIMA Hunan University of Technology Zhuzhou People's Republic of China
2 shared publications - Haixin Zhou
College of Railway Transportation, Hunan University of Technology 1 , Zhuzhou 412008, China
1 shared publication - Jiahao Deng
College of Railway Transportation, Hunan University of Technology 1 , Zhuzhou 412008, China
1 shared publication Jiaojiao Liang
Hunan University of Technology, Zhuzhou, China
1 shared publication- Jiaojiao Liang
College of Railway Transportation, Hunan University of Technology, Zhuzhou 412008, China
1 shared publication
Latest Publications
- Open Access
- 11 September 2024
- Published by Springer Nature in npj Computational Materials
- Vol. 10 (1) , 1-11
Construction frontier molecular orbital prediction model with transfer learning for organic materials
- 11 September 2024
- Published by Springer Nature in npj Computational Materials
- Vol. 10 (1) , 1-11
Open AccessConstruction frontier molecular orbital prediction model with transfer learning for organic materials
- Open Access
- 1 June 2024
- Published by AIP Publishing in AIP Advances
- Vol. 14 (6)
Exploring the impact of fabrication parameters in organic solar cells with PM6:Y6 using machine learning
The preparation parameters of PM6:Y6 non-fullerene organic solar cells (OSCs) have significant influence on the power conversion efficiency (PCE). Herein, machine learning (ML) models are applied for analyzing the quantitative effects of the PCE on PM6:Y6 non-fullerene OSCs from the perspective of fabrication parameters. Random Forest (RF) model has the best evaluation performance and is considered as the best model among the six different algorithms. The Pearson correlation coefficient, coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error of the test set in the RF model are 0.836, 0.668, 0.695, 0.538, and 0.035, respectively. In addition, the most important preparation parameters for PCE are the effective area of OSCs, type of solvent and solvent additives, spin-coating speed, total concentration, annealing temperature, annealing time from SHAP analysis. The optimized PCE can be obtained in PM6:Y6 non-fullerene OSCs when the effective area is between 4 and 6 mm2, the spin-coating speed is 3000 rpm, and the total concentration is 18 mg ml−1, respectively. The contour map further reflects that the optimal annealing temperature are 80 or 105 °C, and the corresponding annealing time are 5 or 9 min, respectively. Based on the above preparation conditions, we verified through the experimental design that the optimal PCE is 16.91%, and the relative error with the ML prediction is only 1.24%. Our research provides low time and cost guidance on preparation parameter matching for the development of high-performance the PM6:Y6 non-fullerene OSCs.
- 1 June 2024
- Published by AIP Publishing in AIP Advances
- Vol. 14 (6)
Open AccessExploring the impact of fabrication parameters in organic solar cells with PM6:Y6 using machine learning
The preparation parameters of PM6:Y6 non-fullerene organic solar cells (OSCs) have significant influence on the power conversion efficiency (PCE). Herein, machine learning (ML) models are applied for analyzing the quantitative effects of the PCE on PM6:Y6 non-fullerene OSCs from the perspective of fabrication parameters. Random Forest (RF) model has the best evaluation performance and is considered as the best model among the six different algorithms. The Pearson correlation coefficient, coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error of the test set in the RF model are 0.836, 0.668, 0.695, 0.538, and 0.035, respectively. In addition, the most important preparation parameters for PCE are the effective area of OSCs, type of solvent and solvent additives, spin-coating speed, total concentration, annealing temperature, annealing time from SHAP analysis. The optimized PCE can be obtained in PM6:Y6 non-fullerene OSCs when the effective area is between 4 and 6 mm2, the spin-coating speed is 3000 rpm, and the total concentration is 18 mg ml−1, respectively. The contour map further reflects that the optimal annealing temperature are 80 or 105 °C, and the corresponding annealing time are 5 or 9 min, respectively. Based on the above preparation conditions, we verified through the experimental design that the optimal PCE is 16.91%, and the relative error with the ML prediction is only 1.24%. Our research provides low time and cost guidance on preparation parameter matching for the development of high-performance the PM6:Y6 non-fullerene OSCs.