Automated Atomic Force Microscopy Using Large Language Models
Submission Track: LLMs for Materials Science - Short Paper
Submission Category: Automated Material Characterization
Keywords: Atomic force microscopy, large language models, automated characterization
TL;DR: Here, we demonstrate an agentic framework that automates atomic force microscopy using large language models
Abstract: Atomic force microscopy (AFM) is a widely used tool for characterizing material surfaces. Here, we present a framework, namely, artificially intelligent lab assistant (AILA), which enables the automation of AFM experiments using large language model-based (LLMs) agents. To evaluate the performance of AILA, we present the first benchmarking dataset, AFMBench, which consists of 100 manually curated tasks corresponding to real-world AFM experiments. These include single-step, multi-step, and mathematical reasoning-based tasks that critically analyze the ability of AILA to perform AFM experiments. Finally, we present two automated experiments using AILA: first, the calibration of the AFM, and second, the imaging of a graphene step. The results presented here highlight the capability of LLMs to guide automated high throughput experiments, accelerating the materials characterizations.
AI4Mat Journal Track: Yes
Submission Number: 53
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