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Autonomous Robotic Metallurgist

Autonomous Robotic Metallurgist

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Autonomous Robotic Metallurgist
Autonomous Robotic Metallurgist
Materials Innovations Platform
  • Publications

    Bayesian optimization with active learning of design constraints using an entropy-based approach

    The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must…

    Read More Bayesian optimization with active learning of design constraints using an entropy-based approachContinue

  • Publications

    High-throughput alloy and process design for metal additive manufacturing

    Many engineering alloys originally designed for conventional manufacturing lack considerations for additive manufacturing (AM), presenting opportunities for novel alloy designs. Evaluating alloy printability requires extensive…

    Read More High-throughput alloy and process design for metal additive manufacturingContinue

  • Publications

    Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design

    Significant advances in theory, simulation tools, advanced computing infrastructure, and experimental frameworks have enabled the field of materials science to become increasingly reliant on computer…

    Read More Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials DesignContinue

  • Publications

    Autonomous efficient experiment design for materials discovery with Bayesian model averaging

    The accelerated exploration of the materials space in order to identify configurations with optimal properties is an ongoing challenge. Current paradigms are typically centered around…

    Read More Autonomous efficient experiment design for materials discovery with Bayesian model averagingContinue

Department of Materials Science & Engineering
Texas A&M University
209 Reed McDonald Building
College Station, TX 77843-3003
Ph: 979-845-0750

[email protected]

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This project is supported by the U.S. National Science Foundation under Cooperative Agreement DMR-2534344. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.

© 2026 Texas A&M University · Department of Materials Science and Engineering.

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