Virtual Scientific Companion for Synchrotron Beamlines: A Prototype
Daniel Potemkin Carlos Soto Ruipeng Li Kevin Yager Esther Tsai
The extraordinarily high X-ray flux and specialized instrumentation at
synchrotron beamlines have enabled versatile in-situ and high throughput
studies that are impossible elsewhere. Dexterous and efficient control of
experiments are thus crucial for efficient beamline operation. Artificial
intelligence and machine learning methods are constantly being developed to
enhance facility performance, but the full potential of these developments can
only be reached with efficient human-computer-interaction. Natural language is
the most intuitive and efficient way for humans to communicate. However, the
low credibility and reproducibility of existing large language models and tools
demand extensive development to be made for robust and reliable performance for
scientific purposes. In this work, we introduce the prototype of virtual
scientific companion (VISION) and demonstrate that it is possible to control
basic beamline operations through natural language with open-source language
model and the limited computational resources at beamline. The human-AI nature
of VISION leverages existing automation systems and data framework at
synchrotron beamlines.