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

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