Abstract: Multi-component alloys have demonstrated excellent performance in various applications, but the vast range of possible compositions and microstructures makes it challenging to identify optimized alloys for specific purposes. To overcome this challenge, large-scale atomic simulation techniques have been widely used for the design and optimization of multi-component alloys. The capability and reliability of large-scale atomic simulations essentially rely on the quality of interatomic potentials that describe the interactions between atoms. This work provides a comprehensive summary of the latest advances in atomic simulation techniques for multi-component alloys. The focus is on interatomic potentials, including both conventional empirical potentials and newly developed machine learning potentials (MLPs). The fitting processes for different types of interatomic potentials applied to multi-component alloys are also discussed. Finally, the challenges and future perspectives in developing MLPs are thoroughly addressed. Overall, this review provides a valuable resource for researchers interested in developing optimized multi-component alloys using atomic simulation techniques.

Key words: Multi-component alloys, Atomic simulation, Empirical potentials, Machine learning potentials