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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
Presentation Title	Quantifying Chemical Short-Range Order in Metallic Alloys
Author(s)	Killian Sheriff, Yifan Cao, Tess Smidt, Rodrigo Freitas
On-Site Speaker (Planned)	Killian Sheriff
Abstract Scope	High-entropy materials are metallic or ceramic systems composed of three or more chemical elements mixed in nearly equiatomic concentrations. These design choices lead to substantial chemical complexity which functions as the background against which microstructural evolution occurs, thereby affecting various material properties through chemistry–microstructure relationships. However, computationally capturing and defining this complexity has remained a challenge and is often overlooked. Here, I will discuss how machine learning, group theory, and statistical mechanics, can be employed together to characterize, atom-by-atom, the state of short-range order of high-entropy materials, thereby advancing the quantitative understanding of metallic alloys, and paving the way for the rigorous incorporation of this phenomenon into mechanical and thermodynamic models.
Proceedings Inclusion?	Planned:
Keywords	High-Entropy Alloys, Machine Learning, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Machine Learning-Assisted Dislocation Density-Based Crystal Plasticity Model for FCC Aluminum

AI-Enabled Upscaling of Ab Initio Thermodynamics for 3C-SiC(100) Surface Reconstructions

An Ultra-Fast Machine-Learning Potentials to Investigate the Phonon-Dislocation Interaction of Lead Selenide

AtomAgents: Alloy Design and Discovery Through Physics-Aware Multi-Modal Multi-Agent Artificial Intelligence

Atomistically-Informed Discrete Dislocation Dynamics Simulations of Shock in Aluminum

Atomistically Informed Mesoscale Modeling of Deformation Behavior of Bulk Metallic Glasses

Bridging Scales in Metal Plasticity: The Roles of Theory, Data Science, and Computing

Coarse-Graining Atomistic Simulation Data with Physics-Guided Gaussian Process Regression

Complex Structure of Liquid and Machine-Learning

Computational Studies on Statistical Features of Dislocation Glide Energetics in Refractory Complex Concentrated Alloys

Developing Data-Driven Dislocation Mobility Laws for BCC Metals

Developing On-Demand, Highly Efficient Digital Twins with DFT Accuracy for Iterative Alloy Discovery Frameworks

Discovering New Mechanisms of Grain Growth with a Machine Learning Model Trained on Experimental and Simulation Data

Efficient High-Throughput Ab Initio Prediction of Liquidus Curves

Engineering the Crack-Tip Material Composition to Enhance the Microplasticity in Refractory Complex Concentrated Alloys

First-Principles Models of Solute-Defect Interactions in Alloys

Influence of Surface Structure on Graphene Formation via Thermal Decomposition of Silicon Carbide

Integrating AI for High-Dimensional Saddle Point Sampling

Interplay between Hydrogen and Screw Dislocation in bcc-Fe: a Neural-network Potential Study

Machine Learning-Enhanced Multiscale Modeling of Solidification

Machine Learning - Kinetic Monte Carlo Investigation on Sluggish Interstitial Diffusion in Fe-Ni-Cr-Cu-Co High Entropy Alloys

Machine Learning for the Efficient Identification of High-Performance Metal-Doped Transition Metal Compounds for Hydrogen Evolution Catalysis

Machine Learning Potentials for Chemically Complex Alloys

Material-Agnostic Training Data Generation for Machine-Learning Interatomic Potentials

Mechanism-Based Data-Driven Exploration of Complex Concentrated Alloys with Enhanced Mechanical Performance

Mesoscale Investigation of Dislocation-Grain Boundary Interactions in Metals and Alloys

Modelling Helium Bubble Evolution and Grain Decohesion in Nanostructured Tungsten Using ML-Based Interatomic Potential

Molecular Dynamic Studies of Strain Rate Effects on Screw Dislocation Mobility In BCC Metals

Multiscale Computation-Experiment Study of Advanced Materials with Characteristic Microstructure

Multiscale Computational Tools and AI Integration Using Chocolate as a Frugal Model System in Self-Driving Lab

Multiscale Modeling for Studying Corrosion-Induced Hydrogen Embrittlement in Zirconium

Neural Network Kinetics: Exploring Diffusion Multiplicity and Chemical Ordering in Compositionally Complex Materials

Pathways to the 7 � 7 Surface Reconstruction of Si(111) Revealed by Machine-Learning Molecular Dynamics Simulations

Peierls-Nabarro Modeling of Dislocations in High Entropy Alloys

Quantifying Chemical Short-Range Order in Metallic Alloys

Realizing High-Throughput Multi-Scale Simulations of Materials Through Machine Learning

Rethinking Materials Simulations; Blending Direct Numerical Simulations with Machine-Learning Strategies

Revealing the Impact of Hydrogen on Iron: Large-Scale Quantitative Atomistic Analysis with Highly Accurate and Transferrable Machine Learning Interatomic Potentials

Simulation-Informed Models for Amorphous Metal Mechanical Property Prediction

Study of Xe Binding in Ag-Exchange Chabazite for Radio-Nuclide Absorption

Surrogate Models in First-Principles Statistical Mechanics Methods

The Connection Between Atomistic Defect Clusters and Geometrically Necessary Dislocations in Irradiated Nanocrystals

UF3: Fast and Interpretable MLIP for High-Performance Molecular Dynamics

Understanding Microstructural Evolution Using Graph Attention Networks

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