About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Interpretable Machine Learning-Assisted Phase Classification for High Entropy Alloys |
Author(s) |
Kyungtae Lee, Mukil V. Ayyasamy, Paige Delsa, Timothy Q. Hartnett, Prasanna V. Balachandran |
On-Site Speaker (Planned) |
Kyungtae Lee |
Abstract Scope |
High entropy alloys (HEAs) are multicomponent materials with nearly equal amount of four or more principal elements. HEAs exhibit excellent mechanical, thermal, and electrochemical properties with immense potential for materials design due to a vast compositional space. In this work, we develop a machine learning (ML) approach with post hoc interpretability capability for HEA phase prediction. The ML methods establish a quantitative relationship between chemical composition and experimentally determined phase information. The interpretability methods offered unprecedented insights into the local behavior (i.e., each composition) of the trained black-box models. We developed a novel algorithm that combined the data from local interpretability analysis with clustering to identify similar compositions. This analysis uncovered previously unknown phase-specific correlations between key features and the HEA phases. We are currently developing novel methods to explore local interpretability of ML models that are trained to predict the mechanical properties of HEAs. |
Proceedings Inclusion? |
Undecided |