  Multi principal element alloys (MPEAs) comprise a unique class of metal
alloys. MPEAs have been demonstrated to possess several exceptional properties,
including, as most relevant to the present study, a high corrosion resistance.
In the context of MPEA design, the vast number of potential alloying elements
and the staggering number of elemental combinations favours a computational
alloy design approach. In order to computationally assess the prospective
corrosion performance of MPEA, an approach was developed in this study. A
density functional theory (DFT) based Monte Carlo method was used for the
development of MPEA structure, with the AlCrTiV alloy used as a model.
High-throughput DFT calculations were performed to create training datasets for
surface activity towards different adsorbate species: O2-, Cl- and H+. Machine
learning (ML) with combined representation was then utilised to predict the
adsorption and vacancy energies as descriptors for surface activity. The
capability of the combined computational methods of MC, DFT and ML, as a
virtual electrochemical performance simulator for MPEAs was established and may
be useful in exploring other MPEAs.
