Elucidating Morphology‐Mobility Relationships of Organic Thin Films Through Transfer Learning‐Assisted Multiscale Simulation

Abstract Understanding the relationship between morphology and charge transport capability in organic thin films is vital for advancements in organic electronics. However, accurately predicting charge mobility in these films is challenging due to the extensive evaluations of transfer integral required. To address this challenge, transfer learning techniques are employed to develop machine learning models capable of efficiently and accurately calculating transfer integrals in organic thin films with grain boundaries and polymorphs. Through machine learning‐assisted multiscale simulations of charge transport, the impact of solution shearing conditions is investigated on the morphology and mobility of organic thin films. The findings reveal that shearing‐induced molecular orientation and pre‐aggregation have a significant influence on film morphology, and a moderate shearing speed combined with a suitable solvent promotes the formation of extended transport networks, leading to higher mobility. The utilization of transfer learning‐accelerated simulation techniques opens up new possibilities for exploring the relationship between solution‐processing conditions, morphology, and charge transport properties of organic thin films. This research provides valuable insights that can be applied to optimize solution‐processing techniques in organic electronics.