Many organic electronics applications such as organic solar cells or thermoelectric generators rely on PEDOT:PSS as a conductive polymer that is printable and transparent. It was found that doping PEDOT:PSS with sorbitol enhances the conductivity through morphological changes. However, the microscopic mechanism is not well understood. In this work, we combine computational tools with machine learning to investigate changes in morphological and electronic properties of PEDOT:PSS when doped with sorbitol. We find that sorbitol improves the alignment of PEDOT oligomers, leading to a reduction of energy disorder and an increase in electronic couplings between PEDOT chains. The high accuracy (r ² > 0.9) and speed up of energy level predictions of neural networks compared to density functional theory enables us to analyze HOMO energies of PEDOT oligomers as a function of time. We find a surprisingly low degree of static energy disorder compared to other organic semiconductors. This finding might help to better understand the microscopic origin of the high charge carrier mobility of PEDOT:PSS in general and potentially help to design new conductive polymers.

许多有机电子应用，例如有机太阳能电池或热电发电机，都依赖PEDOT：PSS作为可印刷和透明的导电聚合物。已经发现，用山梨糖醇掺杂PEDOT：PSS可通过形态变化增强电导率。然而，微观机制尚不为人所知。在这项工作中，我们将计算工具与机器学习相结合，以研究掺入山梨糖醇时PEDOT：PSS的形态和电子性质的变化。我们发现山梨糖醇改善了PEDOT低聚物的排列，导致能量紊乱的减少和PEDOT链之间电子偶联的增加。高精度（r ²> 0.9），并且与密度泛函理论相比，神经网络的能级预测更快，使我们能够分析PEDOT低聚物的HOMO能量随时间的变化。与其他有机半导体相比，我们发现静态能量无序度低得令人惊讶。这项发现可能有助于更好地了解PEDOT：PSS高电荷载流子迁移率的微观起源，并可能有助于设计新的导电聚合物。