About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Artificial Intelligence Applications in Integrated Computational Materials Engineering
|
Presentation Title |
AI in ICME: Methodologies for AI Alignment and Explainability in Self-Driving Labs |
Author(s) |
Kinston Ackölf, Taylor D Sparks |
On-Site Speaker (Planned) |
Kinston Ackölf |
Abstract Scope |
This study investigates the integration of AI with Integrated Computational Materials Engineering (ICME) using chocolate as a frugal twin and proof of concept. In-situ Raman spectroscopy within an autonomous lab collects real-time data, analyzed by AI/ML models to address challenges in materials characterization and design. By leveraging AI, we enhance the efficiency and sustainability of materials manufacturing processes. The study also examines the ethical use of AI, focusing on data transparency and explainability. Our findings highlight AI's transformative potential in revolutionizing materials science and setting new methodologies for ICME practices in self-driving labs and autonomous research systems. |
Proceedings Inclusion? |
Planned: |
Keywords |
ICME, Machine Learning, Other |