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Meeting MS&T25: Materials Science & Technology
Symposium Enhancing the Accessibility of Machine Learning-Enabled Experiments
Presentation Title Autonomous Atomic Force Microscopy using Large Language Model Agents
Author(s) N M Anoop Krishnan, Indrajeet Mandal
On-Site Speaker (Planned) N M Anoop Krishnan
Abstract Scope Self-driving laboratories (SDLs) promise to revolutionize materials research, yet remain limited by rigid protocols. We present AILA (Artificially Intelligent Lab Assistant), a framework automating atomic force microscopy through LLM-driven agents. Our AFMBench evaluation suite comprehensively tests AI agents across the scientific workflow, from experiment design to analysis. We benchmark several leading LLMs (Claude, GPT, Gemini, LLaMa) against human performance, revealing significant limitations. Our assessment demonstrates that current language models struggle with basic tasks like documentation retrieval and show concerning tendencies to deviate from instructions. These challenges intensify in multi-agent scenarios, highlighting critical safety alignment concerns. We showcase AILA's capabilities through increasingly complex AFM applications including calibration, feature detection, and property measurement. Our findings underscore the need for rigorous benchmarking before deploying AI laboratory assistants in scientific research.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Accelerating Scientific Discovery with Machine Learning: Data Analysis for Computational Beamlines
Adaptive Workflows for Lab of the Future
ATOMIC: Autonomous Characterization of 2D Materials Through Foundation Models
Autonomous Atomic Force Microscopy using Large Language Model Agents
DiffractGPT: Atomic Structure Determination from X-ray Diffraction Patterns Using a Generative Pretrained Transformer
Foundational Workflows for Processing Legacy Data and Realizing Domain-Specific Multi-Modal AI Models
From automated to autonomous – creating a general active learning service for self-driving laboratories
High-throughput, Ultra-fast Laser Sintering of Ceramics and Machine-learning Based Prediction on Processing-Microstructure-Property Relationships
Hypothesis Formation and Predictive Modeling of 2D Perovskite Spacer Cations Using Retrieval Augmented LLMs and Deep Kernel Learning
Ptychography Data Pipelines at the Advanced Photon Source
Pycroscopy, AEcroscopy, and data workflows: integrating customized control, data analysis and workflows in an autonomous microscopy facility

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