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  4. Data-driven multiscale modeling of self-assembly and hierarchical structural formation in biological macro-molecular systems
 
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Data-driven multiscale modeling of self-assembly and hierarchical structural formation in biological macro-molecular systems

Publikationstyp
Conference Paper
Date Issued
2023
Sprache
English
Author(s)
Depta, Philipp Nicolas 
Dosta, Maksym  
Heinrich, Stefan  
Herausgeber*innen
Nagel, Wolfgang E.  
Kröner, Dietmar  
Resch, Micheal  
Other Contributor
Höchstleistungsrechenzentrum Stuttgart (HLRS)  
Institut
Feststoffverfahrenstechnik und Partikeltechnologie V-3  
TORE-URI
http://hdl.handle.net/11420/14706
Start Page
513
End Page
529
Citation
International Conference on High Performance Computing in Science and Engineering (HPCSE 2021)
Contribution to Conference
International Conference on High Performance Computing in Science and Engineering, HPCSE 2021
Publisher DOI
10.1007/978-3-031-17937-2_32
Scopus ID
2-s2.0-85160481664
Publisher
Springer
Peer Reviewed
true
DDC Class
004: Informatik
Funding(s)
Teilprojekt von SPP 1934: Multiskalige modellgestützte Untersuchungen funktionaler Enzym- und Proteinagglomerate für biotechnologische Anwendung - Teil 2: Von der Struktur zur Funktion  
Funding Organisations
Deutsche Forschungsgemeinschaft (DFG)  
More Funding Information
The authors would like to acknowledge the German Research Foundation (DFG) for funding within the focus program SPP 1934 (HE 4526/19-2), as well as the HighPerformance Computing Center Stuttgart (HLRS, Acid 44178) for providing the computational resources.
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