Physics-informed machine learning prediction of the martensitic transformation temperature for the design of “NiTi-like” high entropy shape memory alloys

Thiercelin, Léo; Peltier, Laurent; Meraghni, Fodil

doi:10.1016/j.commatsci.2023.112578

Computational Materials Science

2024

DOI: 10.1016/j.commatsci.2023.112578

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Physics-informed machine learning prediction of the martensitic transformation temperature for the design of “NiTi-like” high entropy shape memory alloys

Léo Thiercelin,

Laurent Peltier,

Fodil Meraghni

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Physics-informed machine learning prediction of the martensitic transformation temperature for the design of “NiTi-like” high entropy shape memory alloys

Cited by 3 publications

References 46 publications

Classified dataset, regression and machine learning modeling for prediction of phase transformation temperatures in steels

Classified dataset, regression and machine learning modeling for prediction of phase transformation temperatures in steels

Data-driven study of composition-dependent phase compatibility in NiTi shape memory alloys

Multifunctional high-entropy materials

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