An Improved Random Forest Magnesium Alloy Prediction Method Based on Particle Swarm Optimization

With the development of the material databases’ construction, the use of machine learning methods to process data mining to discover new materials has gradually become a hot topic. The mechanical properties of Mg alloys are related to their components and processing technologies, therefore, it is possible to build prediction model between components, processing technologies and mechanical properties. In order to improve the design efficiency of Mg alloys, using machine learning methods to build a prediction model for the mechanical properties of Mg alloys is of vital importance. To achieve efficient material design, this paper proposed an improved random forest (RF) method based on the Particle Swarm Optimization (PSO) algorithm, and built a Mg alloy performance prediction model. Experiments showed that the accuracy was greatly improved compared with the original RF model, and the prediction accuracy of mechanical properties can reach more than 90%.