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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

Beyond Atom-Centric: A Transformer-Based Approach for Crystal Structure Prediction

Author(s)

Aoi Watanabe, Ryuhei Sato, Yasushi Shibuta

On-Site Speaker (Planned)

Aoi Watanabe

Abstract Scope

Molecular dynamics simulations play a critical role in understanding the microstructure evolution at the atomic scale. So far, methods like common neighbor analysis and polyhedral template matching were used to classify the structure during these processes. However, these methods are facing a difficulty identifying structures with large thermal fluctuations like those near at the melting point. This limitation affects the accuracy of the solid-liquid interface determination during the solidification process. This study proposes a new method for directly estimating crystal structures within each micro-region of a system, using a Transformer, a core component of GPT models. Unlike atom-centric techniques, this model leverages a self-attention mechanism applied to the positional relationships among all atoms within a region. This enables the evaluation of crystal structures at any arbitrary point in coordinate space, thereby facilitating a more precise estimation of interface positions. This advancement promises to enhance our understanding of complex material behaviors.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation,

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