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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Verification, Validation, and Uncertainty Quantification for Self-Driving Labs - Proof of Concept with Chocolate as Frugal Twin
Author(s) Kinston Ackolf, Taylor D Sparks
On-Site Speaker (Planned) Kinston Ackolf
Abstract Scope This study focuses on verification, validation, and uncertainty quantification (VVUQ) for self-driving labs using chocolate as a frugal twin. Implementing in-situ Raman spectroscopy within an autonomous lab, we generate extensive datasets for AI/ML analysis. The research emphasizes the development of robust AI models and their validation through experimental and computational methods. By addressing VVUQ, we enhance the reliability and predictive capabilities of AI-driven materials discovery and design processes. This proof of concept bridges theory and experiment, showcasing the transformative potential of self-driving labs in accelerating materials science advancements.
Proceedings Inclusion? Planned:
Keywords Process Technology, Machine Learning, Other

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Verification, Validation, and Uncertainty Quantification for Self-Driving Labs - Proof of Concept with Chocolate as Frugal Twin
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