Phase Prediction Study of High-Entropy Energy Alloy Generation Based on Machine Learning

Traditional energy sources such as fossil fuels can cause environmental pollution on the one hand, and on the other hand, there will be a shortage of diminishing stocks. Recently, a variety of new energy sources have been proposed by scientists, such as nuclear energy, hydrogen energy, wind energy, water energy, and solar energy. There are already many technologies for converting and storing energy generated from new energy systems, such as various storage batteries. One of the keys to the commercialization of these new energy sources is to explore new materials. Researchers have performed a lot of research on new energy material preparation, mechanical properties, radiation resistance, energy storage, etc. However, new energy metal materials are still unable to combine radiation resistance, good mechanical properties, excellent energy storage, and other characteristics. There is still a lack of breakthrough materials with better performance or more stable structure. Recently, researchers have discovered that high-entropy alloys have become one of the most promising new energy metal materials. Because it not only has high energy storage and high strength, but also has high stability and high radiation resistance, and is easy to form a simple phase, the prediction of phases in high-entropy energy alloys is very critical, and the generation of designed phases in high-entropy energy alloys is a very important step. In this study, three machine learning algorithms were used to predict the generated phase classification in high-entropy alloys, namely, support-vector machine (SVM) model, decision tree (DT) model, and random forest (RF) model. The models are optimized by grid search methods and cross-validated, and performance was evaluated with the aim of significantly improving the accuracy of generative phase prediction, and the results show that the random forest algorithm has the best prediction ability, reaching 0.93 prediction accuracy. The ROC (receiver operating characteristic) curve of the model shows that the random forest algorithm has the best classification of solid-solution (SS) phases, where the classification probabilities AUC (area under the curve) area for amorphous phase (AM), intermetallic phase (IM), and solid-solution phase (SS), respectively, are 0.95, 0.96, and 1, respectively, , which can predict the generated phases of high-entropy energy alloys well.