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  Computationally accelerated experimental materials characterization -- drawing inspiration from high-throughput simulation workflows

Stricker, M., Banko, L., Sarazin, N., Siemer, N., Neugebauer, J., & Ludwig, A. (2022). Computationally accelerated experimental materials characterization -- drawing inspiration from high-throughput simulation workflows. arXiv.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-7D1C-A Version Permalink: https://hdl.handle.net/21.11116/0000-000F-D525-7
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 Creators:
Stricker, Markus1, Author
Banko, Lars2, Author
Sarazin, Nik2, Author
Siemer, Niklas3, Author           
Neugebauer, Jörg3, Author           
Ludwig, Alfred4, Author           
Affiliations:
1Interdisciplinary Centre for Advanced Materials Simulation, Ruhr-University Bochum, Universit ̈atsstr. 150, Bochum, 44801, Northrhine-Westfalia, Germany., ou_persistent22              
2Materials Discovery and Interfaces - Institute for Materials, Ruhr-University Bochum, Universit ̈atsstr. 150, Bochum, 44801, Northrhine-Westfalia, Germany., ou_persistent22              
3Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              
4Materials Discovery and Interfaces, Institut für Werkstoffe, Ruhr-Universität Bochum, Germany, ou_persistent22              

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Free keywords: active learning, gaussian process regression, research data management, automation, autonomous discovery
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Language(s): eng - English
 Dates: 2022-12-092022
 Publication Status: Issued
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 Identifiers: DOI: 10.48550/ARXIV.2212.04804
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Title: arXiv
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