Recently, increasing work in the nuclear materials community has indicated the efficacy of this powerful tool in improving the accuracy and efficiency of modeling tools, and prediction of radiation effects such as void swelling and embrittlement based on experimental data. Indeed, more integrations of AI and nuclear materials investigation are widely open for exploration, e.g., guiding the material design and synthesis, multi-parameter optimization, and modeling linear or non-linear relations among physical quantities. In this symposium, we hope to bring together the research in nuclear materials taking advantage of materials informatics.
The topics of interest include both experimental and modeling efforts in the investigation of nuclear materials that involve the application of machine learning methods, such as (not limited to):
• Fundamental defects properties
• Microstructural evolution
• Radiation effects (swelling, hardening, embrittlement, etc)
• Mechanical/Chemical interactions
• Manufacturing and characterization technologies