Analyzing dynamical disorder for charge transport in organic semiconductors via machine learning

TL;DR

This paper integrates machine learning with multiscale simulations to efficiently analyze static and dynamic disorder in organic semiconductors, enhancing the understanding and prediction of charge transport properties.

Contribution

It introduces a machine learning approach into multiscale models to accurately and efficiently study disorder effects in organic semiconductors, aiding virtual materials design.

Findings

Static disorder distribution is highly asymmetrical in many materials.

Dynamic disorder's role is evaluated by comparing energy level fluctuations to hopping rates.

Machine learning improves the computational prediction of materials properties.

Abstract

Organic semiconductors are indispensable for today's display technologies in form of organic light emitting diodes (OLEDs) and further optoelectronic applications. However, organic materials do not reach the same charge carrier mobility as inorganic semiconductors, limiting the efficiency of devices. To find or even design new organic semiconductors with higher charge carrier mobility, computational approaches, in particular multiscale models, are becoming increasingly important. However, such models are computationally very costly, especially when large systems and long time scales are required, which is the case to compute static and dynamic energy disorder, i.e. dominant factor to determine charge transport. Here we overcome this drawback by integrating machine learning models into multiscale simulations. This allows us to obtain unprecedented insight into relevant microscopic materials properties, in particular static and dynamic disorder contributions for a series of application-relevant molecules. We find that static disorder and thus the distribution of shallow traps is highly asymmetrical for many materials, impacting widely considered Gaussian disorder models. We furthermore analyse characteristic energy level fluctuation times and compare them to typical hopping rates to evaluate the importance of dynamic disorder for charge transport. We hope that our findings will significantly improve the accuracy of computational methods used to predict application relevant materials properties of organic semiconductors, and thus make these methods applicable for virtual materials design.