Machine Learning & multiscale simulations: Toward fast screening of organic semiconductor materials

Michael Rinderle, Alessio Gagliardi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Organic semiconductor devices promise cost-efficient processability at low temperatures, but the usually amorphous materials suffer from low charge carrier mobility. The search for high mobility organic semiconductor materials has thrived data science and Machine Learning approaches to screen the vast amount of possible organic materials. We present a multiscale simulation model based on machine learned transfer integrals to compute the charge carrier mobility in organic thin films.

Original languageEnglish
Title of host publication2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021
PublisherIEEE Computer Society
Pages1-2
Number of pages2
ISBN (Electronic)9781665412766
DOIs
StatePublished - 13 Sep 2021
Event2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021 - Turin, Italy
Duration: 13 Sep 202117 Sep 2021

Publication series

NameProceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD
Volume2021-September
ISSN (Print)2158-3234

Conference

Conference2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021
Country/TerritoryItaly
CityTurin
Period13/09/2117/09/21

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