Machine Learning Prediction of the Corrosion Rate of Zinc-Based Alloys Containing Copper, Lithium, Magnesium, and Silver

Abstract

Implementation of machine learning (ML) techniques in materials science often requires large data sets. However, a proper choice of features and regression methods allows the construction of accurate ML models able to work with a relatively small data set. In this work, an extensive, although still limited, experimental data set of corrosion-related properties of Zn-based alloys used in biomedicine was created. On the basis of this data set, a robust and accurate model was built to predict the corrosion behavior of Zn-based alloys. This work highlights the effectiveness of ML methods for assessing the corrosion behavior of Zn-based alloys, which can facilitate their application in bioimplants.