Machine Learning-Based Charge Transport Computation for Pentacene

被引:41
|
作者
Lederer, Jonas [1 ]
Kaiser, Waldemar [1 ]
Mattoni, Alessandro [2 ]
Gagliardi, Alessio [1 ]
机构
[1] Tech Univ Munich, Dept Elect & Comp Engn, Karlstr 45, D-80333 Munich, Germany
[2] CNR IOM SLACS Cagliari, Ist Officina Mat, I-09042 Monserrato, Italy
关键词
charge transport; machine learning; multiscale approach; organic semiconductors; pentacene; FIELD-EFFECT MOBILITY; MM3; FORCE-FIELD; ORGANIC SEMICONDUCTORS; MOLECULAR-MECHANICS; REORGANIZATION ENERGY; CARRIER TRANSPORT; MONTE-CARLO; DYNAMICS; CRYSTALS; POLARIZATION;
D O I
10.1002/adts.201800136
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:11
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