Modeling of Spatially Correlated Energetic Disorder in Organic Semiconductors.

Mesoscale modeling of organic semiconductors relies on solving an appropriately parametrized master equation. Essential ingredients of the parametrization are site energies (driving forces), which enter the charge transfer rate between pairs of neighboring molecules. Site energies are often Gaussian-distributed and are spatially correlated. Here, we propose an algorithm that generates these energies with a given Gaussian distribution and spatial correlation function. The method is tested on an amorphous organic semiconductor, DPBIC, illustrating that the accurate description of correlations is essential for the quantitative modeling of charge transport in amorphous mesophases.

Parameter-free continuous drift-diffusion models of amorphous organic semiconductors.

Continuous drift-diffusion models are routinely used to optimize organic semiconducting devices. Material properties are incorporated into these models via dependencies of diffusion constants, mobilities, and injection barriers on temperature, charge density, and external field. The respective expressions are often provided by the generic Gaussian disorder models, parametrized on experimental data. We show that this approach is limited by the fixed range of applicability of analytic expressions as well as approximations inherent to lattice models. To overcome these limitations we propose a scheme which first tabulates simulation results performed on small-scale off-lattice models, corrects for finite size effects, and then uses the tabulated mobility values to solve the drift-diffusion equations. The scheme is tested on DPBIC, a state of the art hole conductor for organic light emitting diodes. We find a good agreement between simulated and experimentally measured current-voltage characteristics for different film thicknesses and temperatures.

Parametrization of Extended Gaussian Disorder Models from Microscopic Charge Transport Simulations.

Simulations of organic semiconducting devices using drift-diffusion equations are vital for the understanding of their functionality as well as for the optimization of their performance. Input parameters for these equations are usually determined from experiments and do not provide a direct link to the chemical structures and material morphology. Here we demonstrate how such a parametrization can be performed by using atomic-scale (microscopic) simulations. To do this, a stochastic network model, parametrized on atomistic simulations, is used to tabulate charge mobility in a wide density range. After accounting for finite-size effects at small charge densities, the data is fitted to the uncorrelated and correlated extended Gaussian disorder models. Surprisingly, the uncorrelated model reproduces the results of microscopic simulations better than the correlated one, compensating for spatial correlations present in a microscopic system by a large lattice constant. The proposed method retains the link to the material morphology and the underlying chemistry and can be used to formulate structure-property relationships or optimize devices prior to compound synthesis.