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Titel:

Machine Learning–Based Charge Transport Computation for Pentacene

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Lederer, J.; Kaiser, W.; Mattoni, A.; Gagliardi, A.
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 dis...     »
Stichworte:
charge transport machine learning multiscale approach organic semiconductors pentacene
Zeitschriftentitel:
Advanced Theory and Simulations, Volume2, Issue2 February 1800136 2019-02
Jahr:
2019
Jahr / Monat:
2019-02
Quartal:
2. Quartal
Monat:
Feb
Seitenangaben Beitrag:
1-11
Sprache:
en
Volltext / DOI:
doi:10.1002/adts.201800136
WWW:
https://onlinelibrary.wiley.com/doi/10.1002/adts.201800136
Verlag / Institution:
Wiley Online Library
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