TEM Agent: enhancing transmission electron microscopy with modern AI tools
- 1The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Abstract
Recent improvements in large language models (LLMs) have had a dramatic effect on capabilities and productivity across many disciplines involving critical thinking and writing. The development of the model context protocol (MCP) provides a way to extend the power of LLMs to a specific set of tasks or scientific equipment with help from curated tools and resources. Here, we describe a framework called TEM Agent designed for transmission electron microscopy (TEM) that leverages the benefits of LLMs through a MCP approach. We simultaneously access and control several subsystems of the TEM, a data management platform, and high performance computing resources through text-based instructions. We demonstrate the abilities of the TEM Agent to set up and complete intricate workflows using a simplified set of MCP tools and resources accompanying a commercial LLM without any additional training. The use of a framework such as the TEM Agent simplifies access to complex microscope ecosystems comprised of several vendor and custom systems enhancing the ability of users to accomplish microscopy experiments across a range of difficulty levels.
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BibTeX
@article{wall2026agent,
title = {{TEM Agent: enhancing transmission electron microscopy with modern AI tools}},
author = {Morgan K. Wall and Alexander J. Pattison and Edward S. Barnard and Stephanie M. Ribet and Peter Ercius},
journal = {npj Computational Materials},
year = {2026},
month = jun,
doi = {10.1038/s41524-026-02103-z},
url = {https://doi.org/10.1038/s41524-026-02103-z},
note = {CC BY},
}
RIS
TY - JOUR
TI - TEM Agent: enhancing transmission electron microscopy with modern AI tools
AU - Morgan K. Wall
AU - Alexander J. Pattison
AU - Edward S. Barnard
AU - Stephanie M. Ribet
AU - Peter Ercius
PY - 2026
DA - 2026/06/10
JO - npj Computational Materials
DO - 10.1038/s41524-026-02103-z
UR - https://doi.org/10.1038/s41524-026-02103-z
AB - Recent improvements in large language models (LLMs) have had a dramatic effect on capabilities and productivity across many disciplines involving critical thinking and writing. The development of the model context protocol (MCP) provides a way to extend the power of LLMs to a specific set of tasks or scientific equipment with help from curated tools and resources. Here, we describe a framework called TEM Agent designed for transmission electron microscopy (TEM) that leverages the benefits of LLMs through a MCP approach. We simultaneously access and control several subsystems of the TEM, a data management platform, and high performance computing resources through text-based instructions. We demonstrate the abilities of the TEM Agent to set up and complete intricate workflows using a simplified set of MCP tools and resources accompanying a commercial LLM without any additional training. The use of a framework such as the TEM Agent simplifies access to complex microscope ecosystems comprised of several vendor and custom systems enhancing the ability of users to accomplish microscopy experiments across a range of difficulty levels.
SN - 2057-3960
ER -