[Dataset request]: SRO_in_HEAs_2024 #43
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Emailed Y. Cao, as also contact for issue #55 |
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Name
Josh Vita
Email
[email protected]
Dataset name
SRO_in_HEAs_2024
Authors
Yifan Cao, Killian Sheriff, Rodrigo Freitas
Publication link
https://arxiv.org/abs/2401.06622
Data link
No response
Additional links
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Dataset description
Here we consider various approaches for the construction of training data sets for machine learning potentials (MLPs) for CrCoNi and evaluate their performance in capturing SRO and its effects on materials quantities of relevance for mechanical properties, such as stacking-fault energy and phase stability. It is demonstrated that energy accuracy on test sets often does not correlate with accuracy in capturing material properties, which is fundamental in enabling large-scale atomistic simulations of metallic alloys with high physical fidelity. Based on this analysis we systematically derive design principles for the rational construction of MLPs that capture SRO in the crystal and liquid phases of alloys.
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Additional details
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