About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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Computational Thermodynamics and Kinetics
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Presentation Title |
Prediction of High-temperature Elasticity of Tungsten Using Machine Learning and Data-driven Approach |
Author(s) |
Anruo Zhong, Clovis Lapointe, Alexandra M. Goryaeva, Jacopo Baima, Manuel Athènes, Mihai-Cosmin Marinica |
On-Site Speaker (Planned) |
Anruo Zhong |
Abstract Scope |
By the means of machine learning and data-driven approaches, we investigate the elastic properties of tungsten, a ubiquitous material for future energetic systems [1], up to the melting temperature. We are able to explore the atomistic energy landscape of metals with ab initio accuracy up to the melting temperature. The present workflow, which combines the machine learning force fields [2] and the robust free energy sampling, is emphasized in the body-centered cubic tungsten and validated by the available experimental findings at low temperatures. Moreover, we are able to predict elastic properties of tungsten in the range of temperatures that cannot be addressed experimentally due to its high melting point, from 2100 K up to the melting point. [1] K. Arakawa, M.-C. Marinica et al. Nature Mater. 19, 508 (2020) [2] A. M. Goryaeva et al. Phys. Rev. Materials 5, 103803 (2021). |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, High-Temperature Materials |