Machine learning-based model of surface tension of liquid metals: a step in designing multicomponent alloys for additive manufacturing

Abstract

The surface tension (ST) of metallic alloys is a key property in many processing techniques. Notably, the ST value of liquid metals is crucial in additive manufacturing processes as it has a direct effect on the stability of the melt pool. Although several theoretical models have been proposed to describe the ST, mainly in binary systems, both experimental studies and existing theoretical models focus on simple systems. This study presents a machine learning model based on Gaussian process regression to predict the surface tension of multi-component metallic systems. The model is built and tested on available experimental data from the literature. It is shown that the model accurately predicts the ST value of binaries and ternaries with high precision, and that identifying certain trends in the ST values as a function of alloy composition is possible. The ability of the model to extrapolate to higher-order systems, especially novel concentrated alloys (high entropy alloys, HEA), is discussed.