Phase Prediction and Visualized Design Process of High Entropy Alloys via Machine Learned Methodology

High entropy alloys, which contain five or more elements in equal atomic concentrations, tend to exhibit remarkable mechanical and physical properties that are typically dependent on their phase constitution. In this work, a based leaner and four ensemble machine learning models are carried out to predict the phase of high entropy alloys in a database consisting of 511 labeled data. Before the models are trained, features based on the empirical design principles are selected through XGBoost, taking into account the relative importance of each feature. The ensemble learning methods of Voting and Stacking stand out among these algorithms, with a predictive accuracy of over 92%. In addition, the alloy designing process is visualized by a decision tree, introducing a new criterion for identifying phases of FCC, BCC, and FCC + BCC in high entropy alloys. These findings provide valuable information for selecting important features and suitable machine learning models in the design of high entropy alloys.

Automated summary

In this study, five machine learning models were used to predict the phase of high entropy alloys based on a database of 511 labeled data. Features were selected using XGBoost and the ensemble learning methods of Voting and Stacking showed the highest predictive accuracy of over 92%. A decision tree was used to visualize the alloy designing process and provide valuable information for feature selection and suitable machine learning models in high entropy alloy design.