Record link: 
http://hdl.handle.net/20.500.12708/191910
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Title: 
Machine Learning and ab initio molecular dynamics insights into mechanical properties of diborides
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Citation: 
Lin, S., Sangiovanni, D. G., Hultman, L., Mayrhofer, P. H., & Koutna, N. (2023, September 5). Machine Learning and ab initio molecular dynamics insights into mechanical properties of diborides [Conference Presentation]. FEMS EUROMAT 2023, Frankfurt, Germany.
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Publication Type: 
Presentation - Conference Presentation
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Language: 
English
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Authors: 
Lin, Shuyao 
Sangiovanni, D. G. 
Hultman, L. 
Mayrhofer, Paul Heinz 
Koutna, Nikola 
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Organisational Unit: 
E308-01-1 - Forschungsgruppe Werkstoffwissenschaft Dünner Schichten 
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Date (published): 
5-Sep-2023
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Event name: 
FEMS EUROMAT 2023 
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Event date: 
3-Sep-2023 - 7-Sep-2023
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Event place: 
Frankfurt, Germany
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Keywords: 
MLIP
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Research Areas: 
Computational Materials Science: 100%
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Science Branch: 
2030 - Maschinenbau: 20%
2050 - Werkstofftechnik: 80%
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