Machine Learning-Based Predictions of Threshold Displacement Energy in Materials

The threshold displacement energy (E_d) of materials is a crucial parameter for most of the radiation damage models that predict the performance of materials in extreme radiation environments. Traditionally, E_d calculations have been based on complex experiments or computationally expensive methods, such as Molecular Dynamics or ab initio Molecular Dynamics. However, recent advancements in machine learning have opened new avenues for efficiently estimating E_d values for different materials. This work explores the application of machine learning techniques to predict E_d with accuracy and reduced computational cost compared to traditional methods. We discuss the methodology, data sources, and model performance, highlighting the potential of machine learning to revolutionize our understanding of radiation damage in materials.