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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Reconstruction of Polycrystalline Atomic Structure using Diffusion Model

Author(s)

Tomokazu Ishihara, Ryuhei Sato, Yasushi Shibuta

On-Site Speaker (Planned)

Tomokazu Ishihara

Abstract Scope

Machine learning approaches have been widely used to predict physical properties. However, there is still a challenge to directly predict the time evolution of atomic configurations of a large system like polycrystals. In our previous study, we successfully reproduced the time evolution of polycrystalline structure with Variational Autoencoder and Long Short-Term Memory instead of heavy molecular dynamic (MD) simulation with millions of atoms. However, only the continuous probability density of atoms was obtained after the machine learning due to the reduced latent space. Here, we propose a workflow with diffusion model, reconstructing the atomic coordinates from the probability density. New workflow successfully recovered the atomic coordinates including those near grain boundary from Ni and Fe polycrystal obtained by MD simulations. This result opens a new avenue to accelerate the time evolution of atomic simulations, constructing machine-learning-based “continuum” model in a reduced latent space from the atomic-scale ones.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation, Mechanical Properties

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