  Stacking fault energies (SFEs) are vital parameters for understanding the
deformation mechanisms in metals and alloys, with prior knowledge of SFEs from
ab initio calculations being crucial for alloy design. Machine learning (ML)
algorithms employed in the present work demonstrate approximately 80 times
acceleration in predicting generalized stacking fault energy (GSFE), which is
otherwise computationally expensive to obtain directly from density functional
theory (DFT) calculations, particularly for alloys. The features used to train
the ML algorithms stem from the physics-based Friedel model, revealing a
connection between the physics of d-electrons and the deformation behavior of
transition metals and alloys. Predictions based on the ML model are consistent
with experimental data. This model could aid in accelerating alloy design by
offering a rapid method for screening materials based on stacking fault
energies.
