Charge-density based evaluation and prediction of stacking fault energies in Ni alloys from DFT and machine learning

A combination of high strength and high ductility has been observed in multi-principal element alloys due to twin formation attributed to low stacking fault energy (SFE). In the pursuit of low SFE alloys, a key bottleneck is the lack of understanding of the composition-SFE correlations that would guide tailoring SFE via alloy composition. Using density functional theory (DFT), we show that dopant radius, which have been postulated as a key descriptor for SFE in dilute alloys, does not fully explain SFE trends across different host metals. Instead, charge density is a much more central descriptor. It allows us to (1) explain contrasting SFE trends in Ni and Cu host metals due to various dopants in dilute concentrations, (2) explain the large SFE variations observed in the literature even within a given alloy composition due to the nearest neighbor environments in model concentrated alloys, and (3) develop a machine learning model that can be used to predict SFEs in multi-elemental alloys. This model opens a possibility to use charge density as a descriptor for predicting SFE in alloys. Published under an exclusive license by AIP Publishing.

Automated summary

A combination of high strength and high ductility has been observed in multi-principal element alloys due to twin formation attributed to low stacking fault energy (SFE). However, the understanding of composition-SFE correlations in these alloys is still limited. This study shows that dopant radius is not the only descriptor for SFE in dilute alloys, and highlights the importance of charge density as a central descriptor. The development of a machine learning model based on charge density also suggests its potential as a predictor for SFE in multi-elemental alloys.