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About this Abstract
Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
Presentation Title	Machine Learning Potentials for Chemically Complex Alloys
Author(s)	Rodrigo Freitas
On-Site Speaker (Planned)	Rodrigo Freitas
Abstract Scope	Machine learning potentials (MLPs) are a revolutionary approach to develop interatomic potentials with high physical fidelity, effectively bridging the gap between electronic-structure calculations and atomistic simulations of microstructural evolution. Developing MLPs for chemically complex alloys is challenging because of the large number of local chemical configurations that must be accounted for, and their subtle energetic differences. In this talk I will introduce a systematic approach for developing MLPs for chemically complex alloys, including capturing short-range order and its effects on phase-stability and crystal defects. I will show that this approach leads to a predictive framework for evaluation of various materials properties for chemically complex alloys.
Proceedings Inclusion?	Planned:
Keywords	High-Entropy Alloys, Mechanical Properties, Modeling and Simulation

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Atomistically-Informed Discrete Dislocation Dynamics Simulations of Shock in Aluminum

Atomistically informed mesoscale modeling of deformation behavior of bulk metallic glasses

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Coarse-graining atomistic simulation data with physics-guided Gaussian process regression

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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

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Peierls-Nabarro Modeling of Dislocations in High Entropy Alloys

Quantifying chemical short-range order in metallic alloys

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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

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