  Stacking fault energy (SFE) is of the most critical microstructure attribute
for controlling the deformation mechanism and optimizing mechanical properties
of austenitic steels, while there are no accurate and straightforward
computational tools for modeling it. In this work, we applied both
thermodynamic modeling and machine learning to predict the stacking fault
energy (SFE) for more than 300 austenitic steels. The comparison indicates a
high need of improving low-temperature CALPHAD (CALculation of PHAse Diagrams)
databases and interfacial energy prediction to enhance thermodynamic model
reliability. The ensembled machine learning algorithms provide a more reliable
prediction compared with thermodynamic and empirical models. Based on the
statistical analysis of experimental results, only Ni and Fe have a moderate
monotonic influence on SFE, while many other elements exhibit a complex effect
that their influence on SFE may change with the alloy composition.
