A machine learning prediction model for quantitative analyzing the influence of non-radiative voltage loss on non-fullerene organic solar cells-Reference-Cited by-同舟云学术

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A machine learning prediction model for quantitative analyzing the influence of non-radiative voltage loss on non-fullerene organic solar cells

Published:2023-11 Issue: Volume:475 Page:145958
ISSN:1385-8947
Container-title:Chemical Engineering Journal
language:en
Short-container-title:Chemical Engineering Journal

Author:

Huang Di,Wang Kuo,Li Zhennan,Zhou Haixin,Zhao Xiaojie,Peng Xinyu,Wu Jipeng,Liang Jiaojiao,Meng Juan,Zhao Ling

Funder

Natural Science Foundation of Hunan Province

National College Students Innovation and Entrepreneurship Training Program

Education Department of Hunan Province

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,Environmental Chemistry,General Chemistry

Reference74 articles.

1. Recent progress in organic solar cells (Part I material science);Liu;Science China Chemistry,2022

2. Binary organic solar cells breaking 19% via manipulating the vertical component distribution;Wei;Adv. Mater.,2022

3. Recent progress in molecular design of fused ring electron acceptors for organic solar cells;Dey;Small,2019

4. A history and perspective of non-fullerene electron acceptors for organic solar cells;Armin;Adv. Energy Mater.,2021

5. Latest progress on photoabsorbent materials for multifunctional semitransparent organic solar cells;Kini;Adv. Funct. Mater.,2021

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4. Exploring the impact of fabrication parameters in organic solar cells with PM6:Y6 using machine learning;AIP Advances;2024-06-01

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