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Min-Hsuan Lee
Min-Hsuan Lee
Ph.D. Program in Biomedical Artificial Intelligence, National Tsing Hua University
Email verificata su gapp.nthu.edu.tw
Titolo
Citata da
Citata da
Anno
Insights from machine learning techniques for predicting the efficiency of fullerene derivatives‐based ternary organic solar cells at ternary blend design
MH Lee
Advanced Energy Materials 9 (26), 1900891, 2019
942019
Recent advances in solution‐processable organic photodetectors and applications in flexible electronics
Z Lan, MH Lee, F Zhu
Advanced Intelligent Systems 4 (3), 2100167, 2022
722022
Robust random forest based non-fullerene organic solar cells efficiency prediction
MH Lee
Organic Electronics 76, 105465, 2020
672020
MoO 3-induced oxidation doping of PEDOT: PSS for high performance full-solution-processed inverted quantum-dot light emitting diodes
MH Lee, L Chen, N Li, F Zhu
Journal of Materials Chemistry C 5 (40), 10555-10561, 2017
582017
Interface dipole for remarkable efficiency enhancement in all-solution-processable transparent inverted quantum dot light-emitting diodes
L Chen, MH Lee, Y Wang, YS Lau, AA Syed, F Zhu
Journal of Materials Chemistry C 6 (10), 2596-2603, 2018
342018
Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches
MH Lee
Solar Energy 234, 360-367, 2022
332022
A Machine Learning–Based Design Rule for Improved Open‐Circuit Voltage in Ternary Organic Solar Cells
MH Lee
Advanced Intelligent Systems 2 (1), 1900108, 2020
332020
Performance and matching band structure analysis of tandem organic solar cells using machine learning approaches
MH Lee
Energy Technology 8 (3), 1900974, 2020
272020
Machine learning for understanding the relationship between the charge transport mobility and electronic energy levels for n‐type organic field‐effect transistors
MH Lee
Advanced Electronic Materials 5 (12), 1900573, 2019
272019
Identification of host–guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study
MH Lee
Physical Chemistry Chemical Physics 22 (28), 16378-16386, 2020
262020
A versatile solution-processed MoO3/Au nanoparticles/MoO3 hole contact for high performing PEDOT: PSS-free organic solar cells
W Zhang, W Lan, MH Lee, J Singh, F Zhu
Organic Electronics 52, 1-6, 2018
242018
Solution-processable organic-inorganic hybrid hole injection layer for high efficiency phosphorescent organic light-emitting diodes
MH Lee, WH Choi, F Zhu
Optics Express 24 (6), A592-A603, 2016
212016
Predicting and analyzing the fill factor of non-fullerene organic solar cells based on material properties and interpretable machine-learning strategies
MH Lee
Solar Energy 267, 112191, 2024
102024
Interpretable machine learning model for the highly accurate prediction of efficiency of ternary organic solar cells based on nonfullerene acceptor using effective molecular …
MH Lee
Solar Rrl 7 (14), 2300307, 2023
102023
Interpretable machine-learning for predicting power conversion efficiency of non-halogenated green solvent-processed organic solar cells based on Hansen solubility parameters …
MH Lee
Solar Energy 261, 7-13, 2023
92023
Frontier Molecular Orbital Offset as an Empirical Descriptor for Predicting Short Circuit Current of Nonfullerene Organic Solar Cells
MH Lee
Solar RRL, 2300533, 2023
62023
Flexible biodegradable wearables based on conductive leaf networks
MH Lee, KH Teng, YY Liang, CF Ding, YC Chen
Sustainable Materials and Technologies, e01263, 2025
12025
Investigation of the open-circuit voltage of non-fullerene acceptors-based ternary organic solar cells based on interpretable machine-learning approach and chemically inspired …
MH Lee
Journal of Photochemistry and Photobiology A: Chemistry 450, 115430, 2024
12024
One-stone-for-two-birds strategy for upcycling plastic wastes into high-value-added medical consumables via the dip-coating technique
MH Lee, B Hou
Sustainable Materials and Technologies, e01261, 2025
2025
Highly Sensitive Tubular Strain Sensors: from Nanofiber Arrangements and Conductive Carbon Materials Perspectives
W Lan, Q Ding, X Wu, T Zhou, Y Wang, S Gao, SL Qin, W Zhang, M Lee, ...
Materials Today Communications, 111569, 2025
2025
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