High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

TL;DR

This paper presents a high-throughput computational and machine learning approach to identify binary magnesium alloys with enhanced corrosion resistance by screening intermetallics based on thermodynamic stability and hydrogen adsorption properties.

Contribution

It introduces a novel combined first-principles and machine learning strategy for rapid screening of corrosion-resistant Mg alloys, extending to other alloy systems.

Findings

Identified promising Mg intermetallics like Y3Mg, Y2Mg, La5Mg for corrosion resistance.

Developed ML models with RMSE of 0.11 eV for predicting hydrogen adsorption energies.

Provided a scalable method for designing corrosion-resistant alloys.

Abstract

Magnesium (Mg) alloys have shown great prospects as both structural and biomedical materials, while poor corrosion resistance limits their further application. In this work, to avoid the time-consuming and laborious experiment trial, a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics, from both the thermodynamic and kinetic perspectives. The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified. Then, the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated, and the corrosion exchange current density is further calculated by a hydrogen evolution reaction (HER) kinetic model. Several intermetallics, e.g. Y3Mg, Y2Mg and La5Mg, are identified to be promising intermetallics which might effectively hinder the cathodic HER. Furthermore, machine learning (ML) models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function (W_f) and weighted first ionization energy (WFIE). The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error (RMSE) of 0.11 eV. This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion, but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy, which can be extended to ternary Mg alloys or other alloy systems.