Virtual Scientific Companion for Synchrotron Beamlines: A Prototype

TL;DR

This paper presents VISION, a prototype virtual scientific companion that enables natural language control of synchrotron beamline operations, enhancing human-AI interaction for efficient scientific experiments.

Contribution

The work introduces VISION, a novel open-source AI tool that allows natural language control of beamline experiments using limited computational resources.

Findings

Controlled basic beamline operations via natural language.

Leveraged existing automation and data frameworks.

Demonstrated feasibility with open-source language models.

Abstract

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.