Record link: 
http://hdl.handle.net/20.500.12708/232187
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Title: 
Structural optimization in TensErLEED using a parameter tree and R-factor gradients
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Citation: 
Imre, A. M., Haidegger, P., Kraushofer, F., Wanzenböck, R., Hable, T., Tobisch, S., Kienzer, M., Buchner, F., Carrete, J., Madsen, G. K. H., Schmid, M., Diebold, U., & Riva, M. (2026). Structural optimization in TensErLEED using a parameter tree and R-factor gradients. Npj Computational Materials. https://doi.org/10.1038/s41524-026-02318-0
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Publisher DOI: 
10.1038/s41524-026-02318-0
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Imre, Alexandra Mia 
Haidegger, Paul 
Kraushofer, Florian 
Wanzenböck, Ralf 
Hable, Tobias 
Tobisch, Sarah 
Kienzer, Marie 
Buchner, Florian 
Carrete, Jesús 
Madsen, Georg K. H. 
Schmid, Michael 
Diebold, Ulrike 
Riva, Michele 
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Organisational Unit: 
E165-03-1 - Forschungsgruppe Theoretische Materialchemie 
E134-05 - Forschungsbereich Surface Physics 
E192-02 - Forschungsbereich Databases and Artificial Intelligence 
E056-28 - Fachbereich Computational Sustainability 
E056-04 - Fachbereich TU-DX: Towards Applications of 2D Materials 
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML) 
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Journal: 
npj Computational Materials 
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ISSN: 
2057-3960
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Date (published): 
14-Oct-2026
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Number of Pages: 
12
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Publisher: 
NATURE PORTFOLIO
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Peer reviewed: 
Yes
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Keywords: 
tensor-LEED optimization; surface structure determination; JAX/GPU acceleration
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Project title: 
Komplexität in der Materialmodellierung: F 8100 (FWF - Österr. Wissenschaftsfonds) 
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Research Areas: 
Surfaces and Interfaces: 30%
Mathematical and Algorithmic Foundations: 20%
Computational Materials Science: 50%
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Science Branch: 
1040 - Chemie: 50%
1030 - Physik, Astronomie: 25%
2050 - Werkstofftechnik: 25%
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