A New Ternary Phase Diagram Prediction Descriptor Combined with Machine Learning Research

The morphological characteristics of ternary phase diagrams play a pivotal role in optimizing material properties and facilitating the design of novel alloys. In this study, machine learning (ML) is used to predict the number of phases in ternary alloy systems. A new feature descriptor for phase diagram prediction is proposed in ML, which includes the characteristics of element properties, thermodynamic properties of materials and CALPHAD parameters. Initially, this study constructed a dataset comprising various feature descriptors and validated their correctness employing ML models such as LRC, SVM, RFC, Bagging and GBDT. Subsequently, comparing the performance of different models, and the better-performing models Bagging and GBDT were selected for further prediction studies. The models were fine-tuned using grid search and random search methods to optimize their predictive performance. Ultimately, by predicting phase diagram data for multiple ternary systems at different temperatures, the accuracy rate near the temperature range of the given experimental data was approximately 82%. This demonstrates phase diagram descriptors in conjunction with machine learning to predict ternary phase diagram proposed in this study is practicable. The predicted data also provide guidance for experimental determination of phase diagrams and lay the foundation for future material design and optimization.