Key factors governing the device performance of CIGS solar cells: Insights from machine learning-Reference-Cited by-同舟云学术

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Key factors governing the device performance of CIGS solar cells: Insights from machine learning

Published:2021-11 Issue: Volume:228 Page:45-52
ISSN:0038-092X
Container-title:Solar Energy
language:en
Short-container-title:Solar Energy

Author:

Zhu Chengwan^ORCID,Liu Wu,Li Yaoyao,Huo Xiaomin,Li Haotian,Guo Kai,Qiao Bo,Zhao Suling,Xu Zheng,Zhao Honge,Song Dandan^ORCID

Funder

Natural Science Foundation of Beijing Municipality

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment

Reference43 articles.

1. AMPS, 2020. Analysis of Microelectronic and Photonic Structures, http://www.ampsmodeling.org/materials/ncds.htm (accessed 15 December 2020).

2. Investigation of Al2O3 diffusion barrier layer fabricated by atomic layer deposition for flexible Cu(In, Ga)Se-2 solar cells;Bae;Renewable Energy,2013

3. Cu(In, Ga)Se-2 thin film solar cells with buffer layer alternative to CdS;Bhattacharya;Sol. Energy,2004

4. Extracting bulk defect parameters in silicon wafers using machine learning models;Buratti;npj Comput. Mater.,2020

5. Estimation of open-circuit voltage of Cu(In, Ga)Se-2 solar cells before cell fabrication;Chantana;Renew. Energy,2015

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1. Identifying Molecular Structure–Energy Level Quantitative Relationship of Thermally Activated Delayed Fluorescence Materials Using Machine Learning;The Journal of Physical Chemistry C;2023-11-28

2. Unraveling the Effect of Compositional Ratios on the Kesterite Thin-Film Solar Cells Using Machine Learning Techniques;Crystals;2023-11-12

3. Aspects of effectiveness and significance: The use of machine learning methods to study CuIn1-xGaxSe2 solar cells;Solar Energy;2023-10

4. Recent Progress in Materials and Device Design for Semitransparent Photovoltaic Technologies;Advanced Energy Materials;2023-09-05

5. Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells;Advanced Functional Materials;2023-06-20

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