A detailed mechanism of degradation behaviour of biodegradable as-ECAPed Zn-0.8Mg-0.2Sr with emphasis on localized corrosion attack

Pinc, Jan; Školáková, Andrea; Hybášek, Vojtěch; Msallamová, Šárka; Veřtát, Petr; Ashcheulov, Petr; Vondráček, Martin; Duchoň, Jan; McCarroll, Ingrid; Hývl, Matěj; Banerjee, Swarnendu; Drahokoupil, Jan; Kubásek, Jiří; Vojtěch, Dalibor; Čapek, Jaroslav

doi:10.1016/j.bioactmat.2023.04.012

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

Davletshin,

Korznikova,

Kistanov

2024

J. Phys. Chem. Lett.

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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.

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