Conference Tools for 2024 TMS Annual Meeting & Exhibition

Help

About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Materials Genomics Search for Helium-absorbing Nano-phases in Fusion Structural Materials
Author(s)	Ju Li
On-Site Speaker (Planned)	Ju Li
Abstract Scope	Fusion structural materials often transmute under 14.1 MeV fast neutrons, producing helium which embrittles the grain boundary network. Here, it is shown that neutron-friendly and mechanically strong nano-phases with atomic-scale free volume can have low He-embedding energy and >10at% He-absorbing capacity, and can be especially advantageous for soaking up He on top of resisting radiation damage and creep, provided they have thermodynamic compatibility with the matrix phase. Experimental demonstration proves that Eemb is a good ab initio predictor of He shielding potency in nano-heterophase materials. A list of compounds expected to be good He-absorbing nano-phases is presented, taking into account Eemb, the neutron absorption and activation cross-sections, the elastic moduli, melting temperature, the thermodynamic compatibility, and the equilibrium wetting angle of the nano-phases with the Fe matrix. [Adv. Sci. 9 (2022) 2203555; "Machine learning in nuclear materials research," Current Opinion in Solid State and Materials Science 26 (2022) 100975]
Proceedings Inclusion?	Planned:
Keywords	ICME, Nuclear Materials, Additive Manufacturing

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating Characterization of Radiation Driven Processes using Machine Learning Tools

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

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

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

Questions about ProgramMaster? Contact programming@programmaster.org