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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 Agentic Lifestyles in the Era of AI: Research Workflows and Insights from the Vertical Cloud Lab @ BYU
Author(s) Sterling G. Baird, Xavier Zaitzeff, Gage Erickson, Ronnie Guymon, Carl Robison, Sam Charles, William Mulberry, Luke Winters, Marcus Madsen, Audrey Christiansen, Jinkwan Han, Jeff Hill, Seth Leavitt, Timothy Commins, Benjamin Whitney, Finn Albiston, Devora Najjar, Philip Lampkin, Kevin Maik Jablonka
On-Site Speaker (Planned) Sterling G. Baird
Abstract Scope Agents and LLMs are reshaping research workflows, whether for literature search, topic exploration, teaching, learning, code development, manuscript and proposal writing, administrative work, control of robotic systems, and direct usage within materials optimization campaigns. We highlight our lab's use of these tools in four materials research applications: (1) a self-driving lab for critical-mineral-lean, 3D-printable metal alloy powder for aerospace, (2) generative systems prototyping for a low-cost, high-performance powder multi-dosing system, (3) a self-driving lab for mechanical performance optimization of tensegrity-inspired, multi-material structures for planetary landers, and (4) a self-driving lab for studying precipitate-formation reactions on inner walls of oil pipelines. Our workflows are tightly integrated with GitHub, leveraging GitHub Copilot and Claude Code GitHub Actions using frontier models, with issues and pull requests triggering parallelized multi-agent workflows. As agentic lifestyles normalize in science, we are excited to share our successes and challenges in accelerating materials discovery and commercialization.
Proceedings Inclusion? Planned:
Keywords Additive Manufacturing, Powder Materials, Machine Learning

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Agentic AI-Driven Design of Refractory Multi-Principal Element Alloys
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Agentic Resource Allocation for Batch Multi-Objective Bayesian Optimization in Autonomous Materials Discovery
AI-driven theoretical frameworks to determine structure of complex materials and defects from experiments
AI-Enabled High Throughput Discovery of High Entropy Alloys for Extreme Environments
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Assessing Uncertainty and Reproducibility in ML-Based Grain Growth Predictions: Impact of Training Data
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Design of Experiments for Efficient XRF Under Compositional Uncertainty
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Development of Machine-Learning Interatomic Potential forSolute–Dislocation Interactions in Fe–C–Cu Steels
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Interrogating Materials Informatics Models Along Experimentally Relevant Alloying Pathways
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Mag-GPT: From Query to Magnetic Materials Discovery
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Multiscale Modeling of GaN/AlGaN Interfaces with Machine Learning and DFT
Out-of-Distribution Phase Equilibrium Prediction Using Physics-Informed Graph Attention Networks
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Physics-Informed Graph Neural Networks for Predicting Li-Ion Transport in Solid Electrolyte Interphases
Physics-informed Machine Learning Framework for Material Parameters Estimation from Materials Morphology
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