Conference Tools for 2026 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

2026 TMS Annual Meeting & Exhibition

Symposium

Artificial Intelligence Applications in Integrated Computational Materials Engineering (AI-ICME)

Presentation Title

ML-Accelerated ICME Framework for Solid Phase Processing of Nuclear Cladding Materials

Author(s)

Lei Li, Shadab Shaikh, Lukasz Kuna, Jens Darsell, Xiao Li, Eda Aydogan, Stuart Maloy, Ayoub Soulami

On-Site Speaker (Planned)

Lei Li

Abstract Scope

Solid phase processing is an innovative manufacturing technique that enables the fabrication of high-performance advanced nuclear cladding materials with high efficiency and low energy consumption. During SPP, severe shear deformation and elevated temperatures – below material’s melting point – generated through plastic deformation and friction facilitate accelerated solid-state diffusion and dynamic recrystallization, enabling high-performance cladding production in a single step. To better understand the intricate process-structure-property relationships in SPP, an ICME framework has been developed. This multiscale framework uses smoothed particle hydrodynamics (SPH) to predict thermomechanical responses at the macroscale, which serve as inputs for a mesoscale phase-field and crystal-plasticity (PF-CP) model to simulate microstructure evolutions and subsequent material property changes. To enhance computational efficiency, an AE-LSTM machine learning model is integrated into the SPH modeling process. The framework is applied to friction extrusion of oxide dispersion-strengthened (ODS) steels to help obtain superior strength and corrosion resistance under harsh environments.

Proceedings Inclusion?

Planned:

Keywords

ICME, Machine Learning, Process Technology

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

AI Augmented Molecular Dynamics Modeling of Novel Zeolite Nano Resins for Targeted Advanced Water Treatment

AI for Alloy Design in Extreme Environments

AlloyGPT 2.0: Develop A Self-Adaptive Multi-Agent Autonomous Framework for Additive Manufacturable Alloy Design

An Extensible, Data-Driven Platform for Automated Atomic Structure Analysis

Analyzing Microstructural Interactions in Materials Using Explainable Neural Networks

Artificial Intelligence for Additive Manufacturing

Automated Implementation of Axiomatic Design for Materials System Chart Using Large Language Models

Autonomous Fractography Analysis Framework Driven by Large Language Models

Blacksmithing AI: Using a Large Language Model to Predict Workpiece Shapes and Manufacturing Operations for Automated Manufacturing

Breaking Boundaries: Texture-Aware AI for Metallography

Data-Driven Design of Two-Phase Metallic Spinodoids for Thermomechanical Properties

Development of Machine Learning Interatomic Potentials for Ni-Al-Ti Systems to Study γ/γ' Phase Properties in Superalloys

From CALPHAD to AI: High-Throughput Pathways for Functionally Graded Alloy Design and Additive Manufacturing

Generative AI–Driven Process–Structure–Property Optimization for Materials Discovery

Graph Neural Network-Based Forecasting of Atomic Dynamics at Grain Boundaries

H-13: Establishing a Large Language Model-based Systematic Alloy Design Strategy for Advanced Titanium Alloys

H-14: Ionic Transport Properties and Phase Stability of Solid Electrolyte Material Li7La3Zr2O12: A Deep-Neural-Network Molecular Dynamics Investigation

H-15: Machine-Learned Force Field for the Energetic Molecular Crystal LLM-105

Harnessing AI for Prediction of Abnormal Grain Growth Using 3D Experimental Data

Integrating Microstructural, Diffraction, and Compositional Data for Predictive Materials Modeling

Large Language Model Agents for Atomistic Simulation Workflows

Leveraging Large Language Models for Inverse Design of Processing Parameters in Materials Engineering

Machine Learning-Augmented Finite Volume Modeling and Inverse Optimization of Velocity and Pressure Distribution of Bentonite Slurry in Slurry Shield Tunneling Applications

Machine Learning Prediction of Antioxidant Additive Performance from Atomistic Simulations

ML-Accelerated ICME Framework for Solid Phase Processing of Nuclear Cladding Materials

Physics-Constrained Neural Network for Increased Generalizability in Predicting Material Microstructure Evolution

Physics-Informed Neural–Cellular Framework for Predicting Grain Morphology and Microstructural Evolution in Metal Additive Manufacturing Simulation

Quantifying Relationship Between Creep and Microstructure of Metals Using Symbolic Regression

Reward Engineering in AI-Driven Materials Discovery and Design

Robust Physics-Informed Neural Networks for Modeling Oxide Film Growth in Corrosion Science

Toward an Interactive AI-Enabled Platform for Critical Materials Reduction: From Forecasting to Processing

Toward DFT-Accurate Modeling of HfNbTaTiZr High Entropy Alloy Using Moment Tensor Potential

Using Agentic AI Programming Tools for ICME Code Development

Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement