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Magnesium (Mg) alloys have shown great prospects as both structural andbiomedical materials, while poor corrosion resistance limits their furtherapplication. In this work, to avoid the time-consuming and laborious experimenttrial, a high-throughput computational strategy based on first-principlescalculations is designed for screening corrosion-resistant binary Mg alloy withintermetallics, from both the thermodynamic and kinetic perspectives. Thestable binary Mg intermetallics with low equilibrium potential difference withrespect to the Mg matrix are firstly identified. Then, the hydrogen adsorptionenergies on the surfaces of these Mg intermetallics are calculated, and thecorrosion exchange current density is further calculated by a hydrogenevolution reaction (HER) kinetic model. Several intermetallics, e.g. Y3Mg, Y2Mgand La5Mg, are identified to be promising intermetallics which mighteffectively hinder the cathodic HER. Furthermore, machine learning (ML) modelsare developed to predict Mg intermetallics with proper hydrogen adsorptionenergy employing work function (W_f) and weighted first ionization energy(WFIE). The generalization of the ML models is tested on five new binary Mgintermetallics with the average root mean square error (RMSE) of 0.11 eV. Thisstudy not only predicts some promising binary Mg intermetallics which maysuppress the galvanic corrosion, but also provides a high-throughput screeningstrategy and ML models for the design of corrosion-resistant alloy, which canbe extended to ternary Mg alloys or other alloy systems.

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