Record link: 
http://hdl.handle.net/20.500.12708/212517
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Title: 
Atomistic Study of 4H-SiC Using Ab Initio and Machine Learning Techniques
en
Citation: 
Stella, R., Leroch, S., Hössinger, A., Waldhör, D., & Filipovic, L. (2024). Atomistic Study of 4H-SiC Using Ab Initio and Machine Learning Techniques. In PRiME 2024 October 6, 2024 - October 11, 2024 Honolulu, USA. PRiME 2024, Honolulu, HI, United States of America (the). https://doi.org/10.1149/MA2024-02201801mtgabs
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Publisher DOI: 
10.1149/MA2024-02201801mtgabs
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Publication Type: 
Inproceedings - Abstract Book Contribution
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Language: 
English
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Authors: 
Stella, Robert 
Leroch, Sabine 
Hössinger, Andreas 
Waldhör, Dominic 
Filipovic, Lado 
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Organisational Unit: 
E360-01 - Forschungsbereich Mikroelektronik 
E056-04 - Fachbereich TU-DX: Towards Applications of 2D Materials 
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Published in: 
PRiME 2024 October 6, 2024 - October 11, 2024 Honolulu, USA 
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Volume: 
MA2024-02
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Date (published): 
2024
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Event name: 
PRiME 2024 
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Event date: 
6-Oct-2024 - 11-Oct-2024
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Event place: 
Honolulu, HI, United States of America (the)
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Number of Pages: 
2
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Keywords: 
Wide Bandgap Semiconductors; Aluminum Activation in SiC; Machine Learning Interatomic Potentials (ML-IAPs); Gaussian Approximation Potential (GAP); Moment Tensor Potential (MTP)
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Project title: 
Multi-Scale-Prozessmodellierung von Halbleiter-Bauelemente und -Sensoren: 00000 (Christian Doppler Forschungsgesells) 
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Research Areas: 
Modeling and Simulation: 20%
Computational Materials Science: 80%
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Science Branch: 
2020 - Elektrotechnik, Elektronik, Informationstechnik: 100%
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