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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium Materials Informatics to Accelerate Nuclear Materials Investigation
Sponsorship TMS Structural Materials Division
TMS Materials Processing and Manufacturing Division
TMS: Nuclear Materials Committee
TMS: Computational Materials Science and Engineering Committee
TMS: Advanced Characterization, Testing, and Simulation Committee
Organizer(s) Miaomiao Jin, Pennsylvania State University
Yongfeng Zhang, University of Wisconsin
Tiankai Yao, Idaho National Laboratory
Anjana Anu Talapatra, Los Alamos National Laboratory
Luca Messina, CEA Cadarache
Fei Xu, Idaho National Laboratory
Benjamin Thomas Afflerbach, University of Wisconsin-Madison
Scope Data-driven machine learning methods are becoming increasingly useful to accelerate materials discovery and qualification for nuclear applications. The investigation of the association between variables such as structure and performance is always essential in developing strong materials for advanced reactors. However, experiments can be very costly and lengthy considering the reactor environments. Similarly, materials modeling can also face critiques in mode inaccuracy and inefficiency in typical multiscale frameworks. Therefore, how to smartly incorporate the modern development of artificial intelligence (AI) in nuclear materials study will be of strategic significance to accelerate nuclear materials investigation and unlock far more useful materials than traditional Edisonian methods for optimization within a high-dimension parametric space.

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

Abstracts Due 07/15/2023
Proceedings Plan Planned:

Accelerating Characterization of Radiation Driven Processes using Machine Learning Tools
Characterizing Microstructures in Aluminide Coatings Captured in SEM Image with Convolutional Neural Networks
Comparative Analysis of U-Net, Mask R-CNN, and YOLO for Electron Microscopy Image Segmentation and Object Detection in Nuclear Materials
Deep Learning–enabled Computer Vision Pipeline for the Automated Analysis of Helium Bubbles in In-situ Ion Irradiation Transmission Electron Microscopy Experiments
Deep Neural Network for Porosity and Microstructure Analytics of a High Burnup U-10Zr Metallic Solid Fuel
Defect Evolution in Multi-principal Chemically Disordered Alloys from Multiscale Simulations
E-25: Feature Engineering for Construction of High-accuracy Thermal Conductivity Prediction Model for Uranium Compounds
E-26: Materials Genomics Search for Possible Helium-absorbing Nano-phases in Fusion Structural Materials and Experimental Validation
Emergent Molecular Structure and Dynamics of Tetrahedral Liquids Revealed by Neural Network Forcefield Simulations and Neutron Spin Echo Experiments
Few-shot Machine Learning for Automated Analysis of TEM Images of Nuclear Materials
ICME and ML Modeling Framework to Inform U-10%wtMo Fuel Fabrication Processes
Influence of Empirical Potentials on Data Quality in Computational Studies of Zr Alloys
Inverse Uncertainty Quantification of Dispersion Analysis Research Tool (DART) Parameters Necessary for the Calculation of Fission Gas Swelling in U-Mo Fuel
Machine Learning Enhanced Kinetic Monte Carlo Modeling of Molten Salt Corrosion of Ni-Cr Alloys
Machine Learning for Predicting Reactor Pressure Vessel Embrittlement
Materials Genomics Search for Helium-absorbing Nano-phases in Fusion Structural Materials
Modeling Cascade Damage in Tungsten Using Machine Learning SNAP Interatomic Potential: Electron-Phonon Interaction Model
Neural Networks of Defect Kinetics in Refractory Alloys
Optimizing Thermal Conductivity Prediction of Uranium Compounds using Balanced Multiclass Classification
Probing Radiation Induced Interface Metastability Using Deep Learning Object Detection
Putting Artificial Intelligence into Action to Quantify Radiation Effects in Materials
Quantitative Insight to Fission Gas Bubble Distribution and Lanthanide Movement in Irradiated Annular U-10Zr Metallic Fuel Using Deep Learning
Revealing the Story of Defects from Coupled Extreme Environments with Autoencoders and Dense Neural Networks
Scaling Ductility from Microscale to Bulk by Coupling Crystal Plasticity Simulations with 3D Convolutional Neural Networks
Scanning-TEM (STEM) 3D Tomography for Quantification of Radiation Damage in Neutron Irradiated 316L Stainless Steel
Synthetic Data Driven Materials Informatics Methods for Nuclear Materials Characterization
Utilizing Mechanistic Modeling and Uncertainty Analysis to Support Nuclear Fuel Qualification

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