Prediction of thermal conductivity in multi-component magnesium alloys based on machine learning and multiscale computation

Abstract

Magnesium (Mg) alloys have attracted considerable attention as next-generation lightweight thermal conducting materials. However, their thermal conductivity decreases significantly with increasing alloying content. Current methods for predicting thermal conductivity of Mg alloys primarily rely on computationally intensive first-principles calculations or semi-empirical models with limited accuracy. This study presents a novel machine learning approach coupled with multiscale computation for predicting thermal conductivity in multi-component Mg alloys. A comprehensive database of 1,139 thermal conductivity measurements from as-cast Mg alloys was systematically compiled. A multiscale feature set incorporating elemental characteristics, thermodynamic properties, and electronic structure parameters was constructed. Key features, including atomic radius differences, enthalpy, cohesive energy, and the ratio of electronic thermal conductivity to relaxation time, were identified through sequential forward floating selection (SFFS). The XGBoost algorithm demonstrated superior performance, achieving a mean absolute percentage error (MAPE) of 2.16% for low-component ternary and simpler Mg alloy systems. Through L1 and L2 regularization optimization, the model’s extrapolation capability for quaternary and higher-order novel systems was significantly enhanced, reducing the prediction error to 13.60%. This research provides new insights and theoretical guidance for accelerating the development of high thermal conductivity Mg alloys.

Keywords

Magnesium alloys / thermal conductivity / machine learning / multiscale computation

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