Hardness prediction of high entropy alloys with periodic table representation of composition, processing, structure and physical parameters

Abstract

A rational and information-rich material representation is necessary for a machine learning model to successfully predict material properties. In this work, the periodic table representation (PTR) of composition, processing, structure and physical parameters is established and explored for hardness prediction of high entropy alloys. Based on convolutional neural network (CNN) with PTR, hardness prediction, hardness classification and alloy system extrapolation are achieved to demonstrate the prediction performance of the newly proposed model. By further introducing phase classification results with five mutually non-exclusive phases, predicted by CNN coupled with PTR, the results indicate that adding phase information is beneficial for hardness prediction. In addition, the stacking method is used to further improve the accuracy and stability of hardness prediction. The Diebold-Mariano test is used for model comparisons, and the results demonstrate that the improvement in prediction performance caused by information addition is statistically reliable.