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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Defect Evolution in Multi-principal Chemically Disordered Alloys from Multiscale Simulations
Author(s)	Shijun Zhao
On-Site Speaker (Planned)	Shijun Zhao
Abstract Scope	Multi-principal chemically disordered alloys are composed of different principal elements with high concentrations. The most important feature of this class of alloys is extreme chemical disorder. We first established a database of local environment-dependent defect properties. On this basis, we developed cluster dynamics and machine learning methods to describe the different energy characteristics of defects in diverse local environments, and further combined with kinetic Monte Carlo to study the long-range evolution of defects. Our results show that accurately characterizing the dependence of defects on the local atomic environment plays a key role in understanding the long-term defect evolution, which has a dominant role in determining the tendency of defect growth as well as the defect diffusion rate. Therefore, controlling the local atomic environment, and further affecting defect evolution, is an effective method to design radiation-resistant multi-principal materials.
Proceedings Inclusion?	Planned:
Keywords	Nuclear Materials, High-Entropy Alloys, Machine Learning

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