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

Kordt, Pascal; Stodtmann, S.; Badinski, A.; Helwi, Mustapha Al; Lennartz, Christian; Andrienko, Denis

doi:10.1039/c5cp03605d

Cited by 18 publications

(18 citation statements)

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“…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. [25][26][27][28][29][30][31][32][33][34][35][36] Charge hopping depends on the reorganization energy, [37] the transfer integral, [25,26] and molecular orbital energies. Benefiting from the rapid performance of ML, microscopic processes can be described accurately without the need for phenomenological approximations.…”

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confidence: 99%

Machine Learning–Based Charge Transport Computation for Pentacene

Lederer

Kaiser

Mattoni

et al. 2018

Advcd Theory and Sims

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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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confidence: 99%

Machine Learning–Based Charge Transport Computation for Pentacene

Lederer

Kaiser

Mattoni

et al. 2018

Advcd Theory and Sims

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“…Existing off-lattice models take into account all adjacent molecules within a certain radius [43,49]. Depending on the density of sites, this may cause a strong increase in simulation time.…”

Section: Voronoi Tessellationmentioning

confidence: 99%

“…Moreover, the major drawback of an equidistant spacing between lattice points in the cubic model is overcome with the use of the Voronoi set [48]. The local distribution of lattice sites can be imported from MD simulations using the center of masses of the molecules, providing a sophisticated multi-scale simulation method comparable to [42,43]. In the case of available quantum chemical simulations, the localized quantum state could directly be used as the input of the sites.…”

Section: Voronoi Tessellationmentioning

confidence: 99%

“…Multi-scale simulation models combining molecular dynamics and kMC include the distance dependence and directionality captured in the coupling integrals calculated from MD simulations, but did not investigate the effect of polymer chains on mobility [42,43]. Mollinger et al model single charge transport between crystalline regions based on kMC along a generated polymer chain and investigate the impact of the electric field on the single charge mobility [44].…”

Section: Polymer Chainsmentioning

confidence: 99%

“…Using Marcus theory, one implicitly accounts for molecular details including I ij and λ. It is frequently used in multiscale studies, as for given organic molecules, the molecular positions and transfer integrals need to be calculated by molecular dynamics and quantum chemical calculations, respectively [42,43,79,80]. Marcus theory accounts for multiphonon hopping processes.…”

Section: Charge Carriersmentioning

confidence: 99%

See 2 more Smart Citations

Generalized Kinetic Monte Carlo Framework for Organic Electronics

et al. 2018

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Abstract:In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) and light-emitting diodes (OLEDs). Our model generalizes the geometrical representation of the multifaceted properties of the organic material by the use of a non-cubic, generalized Voronoi tessellation and a model that connects sites to polymer chains. Herewith, we obtain a realistic model for both amorphous and crystalline domains of small molecules and polymers. Furthermore, we generalize the excitonic processes and include triplet exciton dynamics, which allows an enhanced investigation of OSCs and OLEDs. We outline the developed methods of our generalized kMC framework and give two exemplary studies of electrical and optical properties inside an organic semiconductor.

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Simulation of Charge Carriers in Organic Electronic Devices: Methods with their Fundamentals and Applications

Zojer

2021

Advanced Optical Materials

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Device modeling is an established tool to design and optimize organic electronic devices, be them organic light emitting diodes, organic photovoltaic devices, or organic transistors and thin‐film transistors. Further, reliable device simulations form the basis for elaborate experimental characterizations of crucial mechanisms encountered in such devices. The present contribution collects and compares contemporary model approaches to describe charge transport in devices. These approaches comprise kinetic Monte Carlo, the master equation, drift‐diffusion, and equivalent circuit analysis. This overview particularly aims at highlighting the following three aspects for each method: i) The foundation of a method including inherent assumptions and capabilities, ii) how the nature of organic semiconductors enters the model, and iii) how major tuning handles required to control the device operation are accounted for, namely temperature, external field, and provision of mobile carriers. As these approaches form a hierarchy of models suitable for multiscale modeling, this contribution also points out less established or even missing links between the approaches.

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

Cited by 18 publications

References 50 publications

Machine Learning–Based Charge Transport Computation for Pentacene

Machine Learning–Based Charge Transport Computation for Pentacene

Generalized Kinetic Monte Carlo Framework for Organic Electronics

Simulation of Charge Carriers in Organic Electronic Devices: Methods with their Fundamentals and Applications

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