A machine learning-gaussian process screening of carbazole based donors to design efficient organic polymers for photovoltaic applications

IF 3 4区生物学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of molecular graphics & modelling Pub Date : 2025-08-18 DOI:10.1016/j.jmgm.2025.109154

Hussein A.K. Kyhoiesh , Ashraf Y. Elnaggar , Mustafa Al-Khafaji , Islam H. El Azab , Nemah H.M. Al-Jubori , Mohamed H.H. Mahmoud , Mohammed Yaqob

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Abstract

Rapid industrialization is creating a serious threat to natural resources due to the overuse of fossil fuels. This is not only destroying the environment, but their extinction is also expected soon. Such a situation has increased the interest of scientists in designing new photovoltaic (PV) materials with tailored applications. In this study, a machine learning (ML)-assisted approach was proposed for the identification of optimal carbazole-based donor materials aimed at increasing the efficiency of organic PVs (OPVs). An extensive dataset comprising 592 carbazole-derived organic compounds was curated from existing literature, and their open circuit voltage (V_oc), is calculated. Through a targeted analysis, the top-performing donors exhibiting the highest V_oc values are identified. These selected donors are subsequently employed to design new TIC-based polymers to contribute a notable enhancement of the V_oc in the resulting PV devices. Results demonstrate ML potential to accelerate the discovery and optimization of organic solar materials, paving the way for the development of more sustainable and effective PV technologies.

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利用机器学习-高斯过程筛选咔唑基供体，设计光伏应用的高效有机聚合物

由于化石燃料的过度使用，快速的工业化正在对自然资源造成严重威胁。这不仅破坏了环境，而且它们的灭绝也很快就会到来。这种情况增加了科学家设计具有定制应用的新型光伏（PV）材料的兴趣。在这项研究中，提出了一种机器学习（ML）辅助方法来识别最佳的卡巴唑基供体材料，旨在提高有机pv （opv）的效率。从现有文献中收集了包含592种咔唑衍生有机化合物的广泛数据集，并计算了其开路电压（Voc）。通过有针对性的分析，确定了Voc值最高的表现最好的捐助者。这些选定的供体随后被用于设计新的基于tic的聚合物，以显著提高光伏器件中的Voc。结果表明，ML有可能加速有机太阳能材料的发现和优化，为开发更可持续、更有效的光伏技术铺平道路。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of molecular graphics & modelling 生物-计算机：跨学科应用

CiteScore

5.50

自引率

6.90%

发文量

216

审稿时长

35 days

期刊介绍： The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.