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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 Revealing the Impact of Hydrogen on Iron: Large-Scale Quantitative Atomistic Analysis with Highly Accurate and Transferrable Machine Learning Interatomic Potentials
Author(s) Shigenobu Ogata
On-Site Speaker (Planned) Shigenobu Ogata
Abstract Scope Experimentally observing the behavior of hydrogen in materials directly is challenging, and this poses a significant barrier to elucidating the impact of hydrogen on materials. Atomic simulations have been employed to address this issue. The accuracy of atomic simulations depends on the choice of interatomic interactions used; particularly in iron – hydrogen system, there is a scarcity of reliable interatomic interaction models capable of adequately representing the interactions of hydrogen with grain boundaries, surfaces, and dislocations in iron, as well as hydrogen diffusion and the dynamics of hydrogen and defects. We have developed a high-efficiency, high-precision machine learning potential for the iron-hydrogen system to overcome this situation. In this presentation, I will introduce the results obtained from large-scale atomic simulations using this machine-learning potential, detailing the effects of hydrogen on defect formation and propagation during plastic deformation and elucidating the mechanisms of crack initiation within grains and at grain boundaries.
Proceedings Inclusion? Planned:
Keywords Modeling and Simulation, Mechanical Properties,

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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