TY - GEN
T1 - Machine Learning & multiscale simulations
T2 - 2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021
AU - Rinderle, Michael
AU - Gagliardi, Alessio
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85116363693&partnerID=8YFLogxK
U2 - 10.1109/NUSOD52207.2021.9541414
DO - 10.1109/NUSOD52207.2021.9541414
M3 - Conference contribution
AN - SCOPUS:85116363693
T3 - Proceedings of the International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD
SP - 1
EP - 2
BT - 2021 International Conference on Numerical Simulation of Optoelectronic Devices, NUSOD 2021
PB - IEEE Computer Society
Y2 - 13 September 2021 through 17 September 2021
ER -