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 leverages the machine learning-based sure independence screening and sparsifying operator (SISSO) method to derive accurate, analytical models for predicting $E_d$ using fundamental 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.

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• Radiation damage
• Machine learning