预测富勒烯型有机太阳能电池中高 JSC 供体分子的机器学习方法

IF 2.8 3区化学 Q3 CHEMISTRY, PHYSICAL

Chemical Physics Letters Pub Date : 2024-10-23 DOI:10.1016/j.cplett.2024.141719

Yumi Morishita , Misato Yarimizu , Masanori Kaneko , Azusa Muraoka

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引用次数: 0

摘要

本研究旨在利用材料信息学确定可增强富勒烯型有机薄膜太阳能电池中JSC的供体分子。在进行主成分分析和随机森林特征选择后，开发了 LASSO 和 Ridge 回归以及 SVR。遗传算法生成了 250 种新的供体分子，SVR 预测：(i) 4H-环戊二烯噻吩；(ii) 含氟结构；(iii) 毗邻噻吩的 C = O 基团可改善 JSC。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Machine learning approach for predicting high JSC donor molecules in fullerene-typed organic solar cells

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Machine learning approach for predicting high JSC donor molecules in fullerene-typed organic solar cells

This study aimed to identify donor molecules that enhance the J_SC in fullerene-type organic thin-film solar cells using materials informatics. After performing principal component analysis and Random Forest for feature selection, LASSO and Ridge regressions and SVR were developed. A genetic algorithm generated 250 new donor molecules, and SVR predicted that (i) 4H-cyclopentadithiophene, (ii) fluorine-containing structures, and (iii) C = O groups adjacent to thiophenes improve the J_SC.

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来源期刊

Chemical Physics Letters 化学-物理：原子、分子和化学物理

CiteScore

5.70

自引率

3.60%

发文量

798

审稿时长

33 days

期刊介绍： Chemical Physics Letters has an open access mirror journal, Chemical Physics Letters: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. Chemical Physics Letters publishes brief reports on molecules, interfaces, condensed phases, nanomaterials and nanostructures, polymers, biomolecular systems, and energy conversion and storage. Criteria for publication are quality, urgency and impact. Further, experimental results reported in the journal have direct relevance for theory, and theoretical developments or non-routine computations relate directly to experiment. Manuscripts must satisfy these criteria and should not be minor extensions of previous work.