Machine Learning–Based Charge Transport Computation for Pentacene. Issue 2 (28th November 2018)

• Record Type: Journal Article
• Authors: Lederer, Jonas; Kaiser, Waldemar; Mattoni, Alessandro; Gagliardi, Alessio
• Is Part Of: Advanced theory and simulations. Volume 2:Issue 2(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-11-28
• Subjects: charge transport -- machine learning -- multiscale approach -- organic semiconductors -- pentacene; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.201800136
• Languages: English
• ISSNs: 2513-0390

Abstract

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. Abstract : A machine learning–based multiscale approach for charge transport simulation in organic semiconductors is developed . Charge transfer integrals are accurately predicted by machine learning based on kernel ridge regression. Hereby, expensive quantum chemical calculations are avoided, while maintaining the prediction error small. With the predicted transfer integrals, charge carrier mobilities are computed for various degrees of spatial disorder.