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机器学习驱动的有机太阳能电池性能预测:以数据为中心的分子设计方法

Victor Dos Reis Rodrigues¹, Víctor de Souza Assumção Bonfim^2,3, Demétrio Antônio da Silva Filho^2,3

¹Faculty of Technology, University of Brasilia, Brasília, 70.910-900, Federal District, Brazil. vreis6327@gmail.com.

Journal of molecular modeling

|October 15, 2025

概括

机器学习模型使用压缩分子指纹预测有机太阳能电池 (OSC) 的性能. 这种方法使下一代有机光伏材料的有效设计成为可能.

科学领域:

材料科学材料科学材料科学
计算化学的计算化学
可再生能源可再生能源是可再生能源.

背景情况:

有机太阳能电池 (OSCs) 对灵活,可持续的能源充满希望.
由于分子多样性和形态效应,OSC性能的预测建模具有挑战性.
准确的预测对于设计高效的有机光伏材料至关重要.

研究的目的:

开发准确且计算价格低廉的机器学习模型,用于预测关键的OSC性能指标.
系统地展示使用基于树的模型的自编码器压缩分子指纹的使用.
探索不同机器学习算法的预测能力在一个大数据集的捐赠者-接受者对.

主要方法:

利用了大批异质连接OSCs的约2500个实验性特征的捐赠者-接受者对的数据集.
使用RDKit和CDK工具包,从SMILES代码中计算出分子指纹.
使用自动编码器进行特征压缩和各种机器学习模型 (例如,基于树的集合,k-最近邻居) 具有超参数优化.

主要成果:

使用压缩描述符 (Pearson r > 0.95) 实现了对功率转换效率 (PCE),开路电压 (V_oc),短路电流 (J_sc) 和填充因子 (FF) 的强大预测准确度.
证明k-最近邻近模型实现了近乎完美的相关性 (r ~ 0.99) 与小误差.
确定富勒烯数据集比非富勒烯数据集更容易建模,指纹编码了重要的结构信息.

结论:

压缩的分子指纹为OSCs的预测建模提供了强大而高效的计算基础.
简单的k-最近邻近模型在训练足够的描述性特征时显示出令人惊的有效性.
这些方法为高通量预测和先进有机光伏材料的指导分子设计提供了可扩展的途径.

关键词:

大量的异质连接.机器学习机器学习有机太阳能电池有机太阳能电池电力转换效率的效率是什么

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