Generation of Database of Polymer Acceptors and Machine Learning-Assisted Screening of Efficient Candidates

IF 2.3 3区化学 Q3 CHEMISTRY, PHYSICAL

International Journal of Quantum Chemistry Pub Date : 2024-10-30 DOI:10.1002/qua.27510

Mudassir Hussain Tahir, Naeem-Ul-Haq Khan, Khalid M. Elhindi

引用次数: 0

Abstract

This paper presents a comprehensive approach for designing polymer acceptors for organic photovoltaic applications through the generation of an extensive database and the application of machine learning (ML) techniques. Over 40 ML models are trained for the prediction of power conversion efficiency (PCE). Histgradient boosting regressor has appeared as best model. Almost 10 k polymers are generated and their PCE values are predicted. The chemical space of polymers has been visualized and analyzed. Cluster analysis revealed significant differences among the selected polymers. Additionally, an assessment of synthetic accessibility for these polymers indicated that the majority can be synthesized with relative ease.

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生成聚合物受体数据库和机器学习辅助筛选高效候选物质

本文介绍了一种通过生成大量数据库和应用机器学习（ML）技术设计有机光伏应用聚合物受体的综合方法。为预测功率转换效率（PCE），训练了 40 多个 ML 模型。Histgradient boosting 回归器成为最佳模型。生成了近 10 k 种聚合物，并预测了它们的 PCE 值。对聚合物的化学空间进行了可视化分析。聚类分析显示了所选聚合物之间的显著差异。此外，对这些聚合物的合成可得性进行的评估表明，大多数聚合物的合成相对容易。

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

International Journal of Quantum Chemistry 化学-数学跨学科应用

CiteScore

4.70

自引率

4.50%

发文量

185

审稿时长

2 months

期刊介绍： Since its first formulation quantum chemistry has provided the conceptual and terminological framework necessary to understand atoms, molecules and the condensed matter. Over the past decades synergistic advances in the methodological developments, software and hardware have transformed quantum chemistry in a truly interdisciplinary science that has expanded beyond its traditional core of molecular sciences to fields as diverse as chemistry and catalysis, biophysics, nanotechnology and material science.