PNNL

Abstract

This comprehensive review examines molecular dynamics simulations as a valuable tool for studying radiation damage in various materials, including metals, alloys, ceramics, and semiconductors. The importance of interatomic potentials in displacement cascade simulations is highlighted, as they significantly impact the accuracy of defect prediction and evolution. While traditional empirical potentials, such as the Embedded Atom Model (EAM), have been widely utilized for their computational efficiency, recent advancements in machine learning potentials (MLPs) offer improved precision by utilizing quantum-mechanical data. The review emphasizes the nuanced effects of temperature, primary knock-on atom (PKA) energy, and material composition on radiation damage, particularly in high-entropy alloys (HEAs), which exhibit unique behaviors and potential for designing radiation-resistant materials. Practical applications are also discussed, with specific recommendations for interatomic potentials suitable for modeling materials used in Tritium Producing Burnable Absorber Rods (TPBARs), crucial for optimizing tritium production and capture. In conclusion, while significant progress has been made, ongoing developments in machine learning potentials hold promise for even greater accuracy and efficiency. Future efforts should focus on further refining these potentials, expanding their applicability, and incorporating multiscale modeling approaches to develop advanced materials capable of withstanding extreme radiation environments.