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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

Extending first-principles simulations of electronic stopping across time and length scales via machine learning

Author(s)

Andre Schleife

On-Site Speaker (Planned)

Andre Schleife

Abstract Scope

Developing accurate models of material responses to intense radiation is crucial for creating radiation-resistant materials and precise materials manipulation at the atomic level, including in the excited electronic state. These models must account for rapid quantum interactions immediately post-irradiation, linking initial excited states to longer-term radiation effects. In this talk I will present my group’s work on generalizing accurate quantum mechanical modeling of electronic stopping using real-time time-dependent density functional theory towards a cost-effective computational description of electronic stopping as ions travel through materials: With little loss of accuracy, we train a machine learning model on high-fidelity quantum mechanical simulation data of electronic stopping. With this approach we aim for multi-scale ion beam modification modeling and show that the million-fold reduced computational cost allows for first-principles Bragg peak simulations.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Modeling and Simulation,

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