Identifying intrinsic factors for ductile-to-brittle transition temperatures in Fe–Al intermetallics via machine learning

Abstract

The determination of ductile-to-brittle transition temperatures (DBTT) in intermetallic compounds is crucial for assessing their practical applications. In this study, we investigate the intrinsic factors influencing the DBTT of Fe–Al intermetallic compounds through feature engineering. We developed and evaluated two machine learning strategies for this task. Comparing the strategy that incorporates all features, including alloy compositions and atomic features, with the strategy utilizing selected features, it is found that the latter demonstrates superior computational efficiency and reduces overfitting. Specifically, surrogate models based on two selected features, namely cohesive energy and ionization energy, enable accurate prediction of the DBTT of Fe–Al intermetallics, achieving an accuracy of 95%. Additionally, through symbolic regression, we derived a functional expression that captures the relationship between variations in the DBTT and the selected features of intermetallic compounds. These findings have the potential to serve as a valuable guide for optimizing intermetallic compounds.

Keywords

• Ductile-to-brittle transition
• Intermetallic compounds
• Machine learning
• Symbolic regression