Scholarlink

<p>To enhance the power conversion efficiency (PCE) of organic photovoltaic (OPV) cells, the identification of high-performance polymer/macromolecule materials and understanding their relationship with photovoltaic performance before synthesis are critical objectives. In this study, we developed five algorithms using a dataset of 1343 experimentally validated OPV NFA acceptor materials. The random forest (RF) algorithm exhibited the best predictive performance for material design and screening. Additionally, we explored a newly developed polymer/macromolecule structure expression, polymer-unit fingerprint (<i>PUFp</i>), which outperformed the molecular access system (MACCS) across diverse machine learning (ML) algorithms. <i>PUFp</i> facilitated the interpretability of structure-property relationships, enabling PCE predictions of conjugated polymers/macromolecules formed by the combination of donor (D) and acceptor (A) units. Our <i>PUFp</i>-ML model efficiently pre-evaluated and classified numerous acceptor materials, identifying and screening the two most promising NFA candidates. The proposed framework demonstrates the ability to design novel materials based on <i>PUFp</i>-ML-established feature/substructure-property relationships, providing rational design guidelines for developing high-performance OPV acceptors. These methodologies are transferable to donor materials, thereby supporting accelerated material discovery and offering insights for designing innovative OPV materials.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月24日

查看全文
  • Xiumin Liu,
  • Xinyue Zhang,
  • Ye Sheng,
  • Zihe Zhang,
  • Pan Xiong,
  • Xuehai Ju,
  • Junwu Zhu,
  • Caichao Ye

摘要

To enhance the power conversion efficiency (PCE) of organic photovoltaic (OPV) cells, the identification of high-performance polymer/macromolecule materials and understanding their relationship with photovoltaic performance before synthesis are critical objectives. In this study, we developed five algorithms using a dataset of 1343 experimentally validated OPV NFA acceptor materials. The random forest (RF) algorithm exhibited the best predictive performance for material design and screening. Additionally, we explored a newly developed polymer/macromolecule structure expression, polymer-unit fingerprint (PUFp), which outperformed the molecular access system (MACCS) across diverse machine learning (ML) algorithms. PUFp facilitated the interpretability of structure-property relationships, enabling PCE predictions of conjugated polymers/macromolecules formed by the combination of donor (D) and acceptor (A) units. Our PUFp-ML model efficiently pre-evaluated and classified numerous acceptor materials, identifying and screening the two most promising NFA candidates. The proposed framework demonstrates the ability to design novel materials based on PUFp-ML-established feature/substructure-property relationships, providing rational design guidelines for developing high-performance OPV acceptors. These methodologies are transferable to donor materials, thereby supporting accelerated material discovery and offering insights for designing innovative OPV materials.