  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.
