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This is a In Vitro Study study.

Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys

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This research develops machine learning interatomic potential for Fe–Cr–Ni alloys, highlighting its advantages over traditional methods.

The study enables efficient atomic simulations of structural, thermal, and mechanical properties of fcc Fe–Cr–Ni alloys.
Using molecular dynamics, important defects like stacking faults and grain boundaries are thoroughly examined.
Leveraging moment tensor potentials ensures rapid calculations with a small training dataset for reliable outcomes.
This work offers a significant step forward from density functional theory in understanding austenitic stainless steel behavior.

Hao et al. (2025) studied this question.

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This research develops machine learning interatomic potential for Fe–Cr–Ni alloys, highlighting its advantages over traditional methods.