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- Titel:
Machine Learning & multiscale simulations: toward fast screening of organic semiconductor materials
- Dokumenttyp:
- Konferenzbeitrag
- Art des Konferenzbeitrags:
- Textbeitrag / Aufsatz
- Autor(en):
- Rinderle, M.; Gagliardi, A.
- 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.
- Kongress- / Buchtitel:
- International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD)
- Kongress / Zusatzinformationen:
- Torino, Italy 13-17 Sep 2021-09
- Verlag / Institution:
- IEEE Explore Library
- Jahr:
- 2021
- Quartal:
- 3. Quartal
- Jahr / Monat:
- 2021-09
- Monat:
- Sep
- Print-ISBN:
- 2158-3234
- E-ISBN:
- 978-1-6654-1276-6
- Serien-ISSN:
- 2158-3242, 2158-3234
- Sprache:
- en
- Volltext / DOI:
- doi:10.1109/NUSOD52207.2021.9541414
- WWW:
- https://ieeexplore.ieee.org/abstract/document/9541414
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