A Data Driven Machine Learning Approach to Prediction of Stacking Fault Energy in Austenitic Steels

Abstract

The Material Genome Initiative (MGI) calls for establishing frameworks and adopting methodologies to accelerate materials discovery and deployment. The Integrated Computational Materials Engineering (ICME) approach and Materials Informatics leveraging materials data are two very important pillars to the initiative. This research is a data driven materials informatics approach to enable an ICME project on steel alloy design. For the alloy design problem there was a need to predict Stacking Fault Energy (SFE) for any untested alloy composition. SFE is a crucial parameter in determining different deformation regimes in austenitic steels. The SFE itself is dependent on the chemical composition and temperature in steels. There has been considerable study on determination of SFE in steels by experimental and computational methods. While the experimental methods investigate an alloy to find SFE, computational models have been constructed to predict SFE for a given composition and temperature. However, it is shown in this thesis that there are large inconsistencies in experimental data, as well as unavailability of robust computational models to predict SFE in truly multicomponent steel alloys. In this work, a data-driven machine learning approach to mine the literature of SFE in steels with the final aim of predicting deformation regimes for potentially unknown and untested alloy compositions has been demonstrated. Algorithms at the fore-front of Machine Learning have been used to visualize the SFE data and then construct classifiers to predict SFE regime in steels. This machine-learning modeling approach can help accelerate alloy discovery of austenitic steels by linking composition to desired mechanical behavior.