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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 Toward an Autonomous Laboratory for Accelerated Alloy Discovery
Author(s) Janith C. Wanni, Avanish Mishra, Saryu Fensin
On-Site Speaker (Planned) Saryu Fensin
Abstract Scope The growing complexity of structural alloy development requires experimental platforms capable of rapidly synthesizing, characterizing, and evaluating materials with minimal human intervention. Here, we present progress toward an end-to-end autonomous laboratory that integrates the complete alloy development workflow within a unified AI-driven framework. The platform combines autonomous arc melting, machining, heat treatment, metallographic preparation, microscopy, and mechanical testing within a coordinated robotic ecosystem. AI agents manage experimental planning, instrument control, data management, and analysis through natural language interactions, while machine vision continuously monitors experimental execution and detects process anomalies to enable reliable unattended operation. Using a model alloy system, we demonstrate coordinated autonomous execution of synthesis, processing, characterization, and data analysis with minimal human intervention. Experimental data are automatically curated to inform subsequent experimental decisions, establishing the foundation for closed-loop optimization. This framework provides a scalable pathway toward autonomous laboratories for accelerated alloy discovery, materials qualification, and manufacturing process development.
Proceedings Inclusion? Planned:
Keywords Characterization, High-Entropy Alloys, Mechanical Properties

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