| Scope |
Extreme environments of energetic particle irradiation and high temperatures push materials outside of their equilibrium states by producing a high density of defects and causing complex transitions, ultimately changing the microstructure and properties of materials. Significant progress has been made over the past decades in both computational and experimental approaches to elucidate the complex processes underlying radiation damage. Advances in computational modeling and artificial intelligence (AI) have revolutionized the scientific understanding and engineering of nuclear materials, enhancing predictions for material behavior under extreme conditions, optimizing experimental design, and accelerating materials discovery. However, ensuring the reliability and applicability of these tools requires rigorous experimental validation and collaborative data sharing.
This symposium seeks to bring together experts from modeling, AI, and experimental materials science to foster cross-disciplinary interactions, present recent advances, and chart future directions.
Topics of interest include, but are not limited to:
• Atomistic and Mesoscale Modeling: State-of-the-art modeling and simulation approaches, including (but not limited to) atomic level simulations, mesoscale methods for nuclear materials, and extended time-scale techniques to bridge experiments and modeling.
• Al and Machine Learning in Nuclear Materials: Interatomic potential development and surrogate modeling.
• Data Analytics and Model Validation: Techniques for data analytics and model validation, e.g., through coupled computational-experimental workflows or high-throughput approaches.
• Multi-scale and Multi-physics Approaches: Linking models from atomistic to engineering scales, uncertainty quantification and validation as related to model development.
• Open Science, Fair Data and Reproducibility: Best practices for data sharing and model benchmarking.
This symposium focuses on method and framework development; studies emphasizing material-specific behavior should be submitted to other appropriate symposia. |