Machine learning-driven analytical models for threshold displacement energy prediction in materials

Understanding the behavior of materials under irradiation is crucial for the design and safety of nuclear reactors, spacecraft, and other radiation environments. The threshold displacement energy (E_d) is a critical parameter for understanding radiation damage in materials, yet its determination often relies on costly experiments or simulations.

This work presents a compilation of threshold displacement energies (E_d) and fundamental material parameters (e.g., density, atomic mass, melting temperature) designed to enable the application of the machine learning-based Sure Independence Screening and Sparsifying Operator (SISSO) method. The goal is to develop accurate, analytical models for predicting E_d based on intrinsic material properties. The models outperform traditional approaches for monoatomic materials, capturing key trends with high accuracy. While predictions for polyatomic materials highlight challenges due to dataset complexity, they reveal opportunities for improvement with expanded data. This study identifies cohesive energy and melting temperature as key factors influencing E_d, offering a robust framework for efficient, data-driven predictions of radiation damage in diverse materials.