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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Resource-Constrained AI Discovery of Manufacturable Wear-Resistant Steels
Author(s) Christopher Stiles, Nam Le, Alex New, Eddie Gienger, Christian Sanjurjo-Rodriguez, Steven Storck, Lisa Pogue, Tom Arbaugh, Pheobe Appel, Gregory Bassen, Ann Choi, Wyatt Bunstine, Denise Yin, Todd Hufnagel , Alexander deJong, Tyrel McQueen, Leslie Hamilton, Ritwik Bose, Sam Scheck
On-Site Speaker (Planned) Christopher Stiles
Abstract Scope Accelerating discovery and scale-up of wear-resistant steels requires an integrated paradigm that connects AI, simulation, high-throughput experimentation, data infrastructure, and manufacturing feedback. This talk explores a closed-loop materials discovery workflow developed first for resource-constrained lunar in situ resource utilization and now extended to terrestrial wear-resistant alloy design. The workflow links knowledgebase-driven material selection, generative AI, physics-based simulation, additive manufacturing, multimodal characterization, and iterative predict-make-measure learning. Our campaigns have explored manufacturable composition spaces for wear-resistant steels under resource and processability constraints, emphasizing rapid downselection, validation, and translation toward scalable manufacturing. Rather than treating discovery and manufacturing as sequential steps, the approach incorporates manufacturability, additive processing constraints, composition-processing-microstructure-property relationships, and characterization feedback from the outset. We demonstrate how this approach enables materials discovery through iterative optimization of chemistry, processing, and microstructure for improved wear performance while building reusable infrastructure for accelerated innovation under real-world constraints.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Computational Materials Science & Engineering, Machine Learning

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