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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

Autonomous Exploration of Nanostructure Evolution in Solid-State Metal Dealloying via Laser-Induced Thermal Gradients and Multimodal Synchrotron X-rays

Author(s)

Cheng-Chu Chung, Ruipeng Li, Carly Zincone, Honghu Zhang, Ming Lu, Siyu Wu, Nikhil Tiwale, Fernando Camino, Marcus Noack, Phillip Maffettone, Bruce Ravel, Kevin Yager, Yu-chen Karen Chen-Wiegart

On-Site Speaker (Planned)

Cheng-Chu Chung

Abstract Scope

Advancing nanoarchitectured materials requires precise control over complex processing conditions. We present an autonomous, data-driven approach that uses machine learning to efficiently explore the processing space for dealloyed thin-film nanostructures. Laser-based heating drives dealloying, while data are collected in situ using synchrotron-based grazing-incidence wide- and small-angle X-ray scattering. Experimental decisions are guided by gpCAM, a Gaussian process-based algorithm that selects informative temperature–time conditions. A multimodal autonomous scheme is further developed, where gpCAM-suggested coordinates are applied in real time to twin samples for simultaneous synchrotron X-ray absorption spectroscopy at a second beamline. This framework enables rapid exploration of high-dimensional design spaces and captures multiscale materials features ranging from chemical bonding, atomic structures, to nanoscale morphology. It also experimentally validates machine learning models identifying effective dealloying systems. Our results demonstrate how integrating domain expertise with artificial intelligence and data-driven tools accelerates autonomous experimentation and materials discovery for advanced nanostructure design.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Nanotechnology, Thin Films and Interfaces

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