Solid-liquid phase transition temperature prediction of alloys based on machine learning key feature screening

Abstract

A machine learning strategy is proposed based on the demand for prediction of solid-liquid phase transition temperature properties of multi-component precious metal alloys. Firstly, the candidate feature set are constructed by mathematical operation of the physical and chemical parameters of the material according to the chemical ratio of the alloy chemical formula. Then, a novel feature selection framework of correlation screening → genetic algorithm screening → feature weight ranking → exhaustive screening is proposed to effectively identify key feature combinations affecting solid-liquid phase transition temperature. Finally, the support vector regression algorithm is used to establish the "key feature combination-solid phase transition temperature" model with an error less than 9.83% and the "key feature combination-liquid phase transition temperature" model with an error less than 9.35%. The proposed new feature framework overcomes the inability of conventional feature selection techniques to simultaneously meet the requirements of interpretability, low computational complexity, and strong feature generality. Moreover, the comprehensive equilibrium complexity and accuracy (R2) constructed "solid temperature-key feature combination" with an error of less than 6.50% and "liquid phase transition temperature-key feature combination" with an error of less than 6.71 % by symbolic regression algorithm, which strengthened the understanding of the influence of key independent variable feature parameters on solid-liquid phase transition temperature prediction. This established machine learning strategy has excellent reliability in the prediction of solid-liquid phase transition temperature of multi-component alloys, and the proposed feature selection framework is expected to develop into a new feature selection technique.