A Computational Approach for Mapping Electrochemical Activity of Multi-Principal Element Alloys
Document Type
Article
Publication Date
5-20-2023
Abstract
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.
DOI
Source Publication
arXiv e-print repository
Recommended Citation
arXiv:2305.12059 [cond-mat, physics:physics]
APA: Yuwono, J. A., Li, X., Doležal, T. D., Samin, A. J., Shi, J. Q., Li, Z., & Birbilis, N. (2023). A Computational Approach for Mapping Electrochemical Activity of Multi-Principal Element Alloys (arXiv:2305.12059). arXiv. https://doi.org/10.48550/arXiv.2305.12059
Comments
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Funding notes: The authors acknowledge the high-performance computing (HPC) resources from the Department of Defense through Air Force Research Laboratory (AFRL) HPC Mustang and from National Computing Infrastructure (NCI) Australia through NCI Gadi. Authors X.L. and J.Q.S. acknowledge the financial support from the Center for Augmented Reasoning, Australian Institute for Machine Learning. Authors T. Dolezal and A. Samin acknowledge the financial support from the Air Force Office of Scientific Research (AFOSR). Financial support from the Office of Naval Research under the contract ONR: N00014-17-1-2807 with Dr. David Shifler and Dr. Clint Novotny as program officers is gratefully acknowledged.