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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

From Atoms to Application: AI-Accelerated Materials Discovery in Constrained Design and Manufacturing Spaces

Author(s)

Christopher Stiles, Michael Pekala, Nam Q Le, Alex New, Kurun Kumar Rao, Milena Graziano, Eddie Gienger, Christian Sanjurjo-Rodriguez, Steven Storck, Elizabeth Pogue, Tom Arbaugh, Pheobe Appel, Avi Bregman, Adrian Podpirka, Chris Ribaudo, Neil Joshi, Gregory Bassen , Ann Choi, Joshua Hummel, Noah Edmiston, Wyatt Bunstine, Denise Yin, Todd Hufnagel, Alexander deJong, Mark Foster, Colin Goodman, Tyrel McQueen, Leslie Hamilton, Morgana Trexler

On-Site Speaker (Planned)

Christopher Stiles

Abstract Scope

We present a closed-loop framework for structural materials discovery that integrates generative AI, predictive modeling, autonomous synthesis, and adaptive characterization, originally designed for lunar in situ resource utilization (ISRU), but generalizable to Earth-based sustainable manufacturing. Generative models including MatterGen, PGCGM, and CDVAE create novel crystal structures under elemental constraints, enabling exploration within bounded, resource-limited design spaces. Structures are relaxed and screened for stability using physics-informed tools such as MatterSim and CHGNET. Mechanical properties like hardness and ductility are predicted using self-supervised models effective in sparse-data regimes. A high-throughput directed energy deposition (DED) system rapidly synthesizes up to 27 compositions per substrate, accelerating validation through novel hardness and ductility proxies. Experimental feedback continually refines model predictions, enabling iterative optimization. The presented framework bridges AI-driven design with scalable physical synthesis, offering a path forward for materials discovery and realization in extreme ISRU environments.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Additive Manufacturing, Mechanical Properties

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