Conference Logo ProgramMaster Logo
Conference Tools for 2027 TMS Annual Meeting & Exhibition
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract
Meeting 2027 TMS Annual Meeting & Exhibition
Symposium Modeling, AI Applications, and Method Development in Nuclear Materials
Presentation Title Atomistic Insights into Pb/Bi-Induced Liquid Metal Embrittlement in Bcc and Fcc Fe
Author(s) Qiuguo Yang, Pär Olsson
On-Site Speaker (Planned) Qiuguo Yang
Abstract Scope Liquid metal embrittlement (LME) is a key degradation mechanism for steels exposed to liquid lead or lead-bismuth eutectic in lead-cooled fast reactors. Experimental observations indicate that ferritic and austenitic steels exhibit different susceptibilities to Pb/Bi-assisted embrittlement, suggesting that crystal structure and grain-boundary character influence LME susceptibility. Here, density functional theory calculations are performed to compare Pb/Bi adsorption, migration, and segregation in bcc and fcc Fe surface and grain-boundary models. Adsorption energies, diffusion barriers, segregation energies, work of separation and charge redistribution are examined to clarify the interactions of Pb/Bi with Fe surfaces and grain boundaries, and to assess how atomic packing, interfacial excess volume, and local bonding contribute to differences in embrittlement potency. This study provides an atomistic framework for understanding why ferritic and austenitic Fe-based alloys may respond differently to Pb/Bi-induced liquid metal embrittlement. The findings will support microscale fracture modeling and the development of LME-resistant Fe-based structural materials.
Proceedings Inclusion? Planned:
Keywords Nuclear Materials, Modeling and Simulation, Iron and Steel

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Cluster Expansion Free Energy Input for CALPHAD Description of δ-PuGa and δ-PuU
A Machine-Learning Interatomic Potential for Defect Migration and Cluster Evolution in Silicon Carbide
A modular phase-field framework coupling microstructure and mechanics for nuclear materials simulation: the INFERNO code
A Novel Computational Approach for the Synergistic Effects in Irradiation Hardening of Alloys
A Physics-Informed Neural Network-Based Phase-Field Model for Simulating Hydride Assisted Cracking
A Strength-Augmented Phase-Field Fracture Framework Using Arbitrary Data-Driven Strength Surfaces: Development and Application to Nuclear Fuels
Actively Learned Physically Constrained Gaussian Process Regression for TRISO Particle in Particle Compact
Advances and Challenges in Microstructure Evolution Models for Nuclear Materials under Irradiation
Advancing the dispersed barrier hardening model to estimate the yield strength of ductile metals
Analysis of Helium Induced Swelling and Temperature Dependence in Nuclear Reactor Stainless Steel Using Rate Theory
Artificial Intelligence meets irradiation Physics: learning defects, entropy and free energies
Atomistic Insights into Pb/Bi-Induced Liquid Metal Embrittlement in Bcc and Fcc Fe
Balancing Radiation Tolerance and Tritium Retention: The Role of Grain Boundaries in LiAlO2 and LiAl5O8
Benchmarking and Refining Machine-Learned Interatomic Potentials for Radiation Damage and Tritium Diffusion in LiAlO2
Calculation of Transport Properties of Nuclear Materials using a First-Principles based approach
Data-driven representations of dislocation networks at the atomic and mesoscale
Deep Learning–Enabled Automated Tracking Pipeline for Helium Cavity Dynamics in In-Situ Transmission Electron Microscopy
Defects and Impurity Properties of VN Precipitates in ARAFM Steels: Modelling using a Universal Machine Learning Potential and Experimental Validation
Developing a Machine Learning Interatomic Potential for FeC under Irradiation
Development of a model for irradiation-assisted grain growth for nanocrystalline UO2
Development of Machine-Learned Interatomic Potentials for Tritium Permeation Barrier Materials as Fusion Structural Materials
Exploring the Extremes: Entropy-Driven Data Generation for Universal Atomistic Simulation
Hydrogen Diffusivity in Nb-based High Entropy Alloys for Fusion Reactors from DFT, kMC and ML
Insights into the coupling between irradiation and corrosion in materials
Integrated Modelling and Advanced Manufacturing of Nuclear Materials for Advanced Reactors
Irradiation-Driven Recrystallization in Fusion-Grade Tungsten: A Mesoscale, Microstructure-Aware Model
Machine Learning-Enhanced Process Modeling Framework for Solid-Phase Processing of Nuclear Cladding Materials
Machine Learning and Calphad Guided Exploration of Low-Neutron-Cross-Section Refractory Multi-Principal Element Alloys
Machine learning application in the design of irradiation-resistant nuclear materials
Machine Learning Predicted Thermal Conductivity of Nuclear Fuels
Mat-Fus: Multimodal LLMs for FAIR Databases in AI-Assisted Fusion Material Design
MOOSE-based Phase-Field Simulation of Copper Pit Growth
Multiphysics Simulations-Guided Design of a Silicon MEMS Testbed for In-Situ Ion Irradiation Creep Testing
Phase-Field Modeling of Grain Restructuring in High-Burnup UO₂ Fuel
Phase-field simulation of telluride formation in Ni–Cr–Te systems for molten salt reactor structural materials
Point Defect Evolution in BCC-Complex Composition Alloys Investigated via Machine-Learning Potentials
Predictive Modeling of Thermal Transport Degradation in Fusion-Relevant Materials
Preparation and Characterization of High-Burnup Simulated UO2 Fuels
Stability and Oxidation Pathways of Metallic CrNb and Ceramic (Cr,Nb)N Coatings
TEM-Informed Molecular Dynamics Simulation of Helium Bubble Evolution at Grain Boundaries
Thermodynamically Consistent Phase-Field Modeling of Molten Salt Corrosion Using CALPHAD-Based Metal-Salt Thermochemistry
Understanding Dislocation Interactions in BCC Fe using Machine-Learned Potentials
Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement