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Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential

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Zhu,  Li-Fang
Ab Initio Thermodynamics, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Institute of Materials Science, University of Stuttgart, Germany;

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Neugebauer,  Jörg
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Grabowski,  Blazej
Institute of Materials Science, University of Stuttgart, Pfaffenwaldring 55, Stuttgart, 70569, Germany;
Computational Phase Studies, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Citation

Zhu, L.-F., Neugebauer, J., & Grabowski, B. (2023). Towards high throughput melting property calculations with ab initio accuracy aided by machine learning potential. Talk presented at CALPHAD L Conference. Cambridge, MA, USA. 2023-06-25 - 2023-06-30.


Cite as: https://hdl.handle.net/21.11116/0000-000F-A9A3-A
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