Conference Tools for 2024 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Scaling Ductility from Microscale to Bulk by Coupling Crystal Plasticity Simulations with 3D Convolutional Neural Networks
Author(s)	Laura Z. Vietz, Carter K. Cocke, Eduardo A. Trevino, Ashley D. Spear
On-Site Speaker (Planned)	Laura Z. Vietz
Abstract Scope	Experimentally characterizing bulk mechanical behavior for certain materials, including nuclear materials used in harsh operating environments, can be challenging and cost prohibitive. Some cases are limited to small-scale testing, leading to size effects for samples below the representative volume element (RVE), or the smallest volume of material above which a property of interest converges to that of bulk material. Volumes smaller than the RVE, called statistical volume elements (SVEs), exhibit scattered responses. This research aims to link microstructure-dependent SVE-derived ductility to bulk-scale ductility by coupling high-throughput numerical simulation with machine learning. As a proof-of-concept, a damage-enabled elasto-viscoplastic fast Fourier transform framework simulated the mechanical response of 3D SVE microstructures for multiple model materials. Images of the SVE microstructures and their corresponding ductility values are used to train a 3D convolutional neural network to predict bulk-scale ductility. This research could enable cost-effective methods for characterizing bulk materials by testing micro/nanoscale specimens.
Proceedings Inclusion?	Planned:
Keywords	Modeling and Simulation, Mechanical Properties, Machine Learning

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Characterizing Microstructures in Aluminide Coatings Captured in SEM Image with Convolutional Neural Networks

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

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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

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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