Record link: 
http://hdl.handle.net/20.500.12708/208145
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Title: 
Machine-learning-backed evolutionary exploration of the SrTiO₃ (110) surface phase diagram
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Citation: 
Wanzenböck, R., Buchner, F., Riva, M., Carrete, J., & Madsen, G. K. H. (2024). Machine-learning-backed evolutionary exploration of the SrTiO₃ (110) surface phase diagram. In 87th Annual Meeting of the DPG and DPG Spring Meeting 2024 of the Condensed Matter Section (SKM) (pp. 777–777).
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Publication Type: 
Inproceedings - Abstract Book Contribution
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Language: 
English
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Authors: 
Wanzenböck, Ralf 
Buchner, Florian 
Riva, Michele 
Carrete, Jesús 
Madsen, Georg Kent Hellerup 
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Organisational Unit: 
E165-03-1 - Forschungsgruppe Theoretische Materialchemie 
E056-04 - Fachbereich TU-DX: Towards Applications of 2D Materials 
E056-23 - Fachbereich Innovative Combinations and Applications of AI and ML (iCAIML) 
E134-05 - Forschungsbereich Surface Physics 
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Published in: 
87th Annual Meeting of the DPG and DPG Spring Meeting 2024 of the Condensed Matter Section (SKM) 
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Volume: 
59
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Date (published): 
21-Mar-2024
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Event name: 
87th Annual Meeting of the DPG and DPG Spring Meeting 2024 of the Condensed Matter Section (SKM) 
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Event date: 
17-Mar-2024 - 22-Mar-2024
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Event place: 
Berlin, Germany
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Number of Pages: 
1
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Keywords: 
SrTiO3; surface reconstruction; machine learning; evolutionary search
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Research Areas: 
Quantum Modeling and Simulation: 30%
Surfaces and Interfaces: 40%
Computational Materials Science: 30%
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Science Branch: 
1040 - Chemie: 100%
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Appears in Collections:
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