Predicting the energetics and kinetics of Cr atoms in Fe-Ni-Cr alloys via physics-based machine learning

Abstract

The energy and activation barrier distributions of Cr atoms in austenitic alloys are investigated over a multiplicity of modeling samples across a wide range of chemical (e.g. solid solutions vs. segregated states) and microstructural (e.g. bulk vs. grain boundaries) environments. Assisted with a physics-based machine learning algorithm, it is found that the thermodynamic and kinetic behaviors of Cr atoms can be reliably predicted according to the local electronegativity (χ) and free volume of local atomic packing (Vv). The corresponding predictive maps in the χ−Vv parameter space are established, which are in line with existing experiments and validated by a parallel modeling with a different interatomic force field. The implications of the present study regarding its potential to guide the design of austenitic alloys with desired properties are also discussed.