Machine Learning–Based Charge Transport Computation for Pentacene

Abstract

Insight into the relation between morphology and transport properties of organic semiconductors can be gained using multiscale simulations. Since computing electronic properties, such as the intermolecular transfer integral, using quantum chemical (QC) methods requires a high computational cost, existing models assume several approximations. A machine learning (ML)-based multiscale approach is presented that allows to simulate charge transport in organic semiconductors considering the static disorder within disordered crystals. By mapping fingerprints of dimers to their respective transfer integral, a kernel ridge regression ML algorithm for the prediction of charge transfer integrals is trained and evaluated. Since QC calculations of the electronic structure must be performed only once, the use of ML reduces the computation time radically, while maintaining the prediction error small. Transfer integrals predicted by ML are utilized for the computation of charge carrier mobilities using off-lattice kinetic Monte Carlo (kMC) simulations. Benefiting from the rapid performance of ML, microscopic processes can be described accurately without the need for phenomenological approximations. The multiscale system is tested with the well-known molecular semiconductor pentacene. The presented methodology allows reproducing the experimentally observed anisotropy of the mobility and enables a fast estimation of the impact of disorder.However, to this day, the main issue concerning organic semiconductors is the low charge carrier mobility compared to their inorganic counterpart, [5] which limits the operational speed and performance of electronic devices. Largest measured mobilities are in the range of 10 cm 2 V −1 s −1 for highly crystalline pentacene [6] and rubrene. [7] Due to the lack of insight into the structureproperties relationships, the design of new materials often relies on chemical intuition. This makes it difficult to identify promising materials with enhanced mobility. Thus, theoretical and numerical models are considered as promising pathways to increase the understanding of the relation between charge transport properties and structural morphologies within organic materials at the nanoscale. [8] Various techniques ranging from analytic approaches [9][10][11][12][13][14] to simulation methods such as molecular dynamics (MD) [15][16][17] and kinetic Monte Carlo (kMC) [18][19][20][21][22][23] are utilized to model the impact of molecular structures on transport properties such as the charge carrier mobility. The above approaches consider different length scales: analytic models provide an empirical picture of charge transport in disordered organic materials at the continuum scale, but they do not account for the different molecular structures; MD simulations yield insight into microscopic properties on an atomistic scale (≤ 1 nm), however it is not feasible to obtain mesoscopic transport properties due to the high computational cost; kMC allows to bridge different length scales by implicitly linking structural an...

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