Large language models (LLMs) are transforming the landscape of chemistry and materials science. Recent examples of LLM-accelerated experimental research include virtual assistants for parsing synthesis recipes from the literature, or using the extracted knowledge to guide synthesis and characterization. Despite these advancements, their application is constrained to labs with automated instruments and control software, leaving much of materials science reliant on manual processes. Here, we demonstrate the rapid deployment of a Python-based control module for a Keithley 2400 electrical source measure unit using ChatGPT-4. Through iterative refinement, we achieved effective instrument management with minimal human intervention. Additionally, a user-friendly graphical user interface (GUI) was created, effectively linking all instrument controls to interactive screen elements. Finally, we integrated this AI-crafted instrument control software with a high-performance stochastic optimization algorithm to facilitate rapid and automated extraction of electronic device parameters related to semiconductor charge transport mechanisms from current-voltage (IV) measurement data. This integration resulted in a comprehensive open-source toolkit for semiconductor device characterization and analysis using IV curve measurements. We demonstrate the application of these tools by acquiring, analyzing, and parameterizing IV data from a Pt/Cr$_2$O$_3$:Mg/$\beta$-Ga$_2$O$_3$ heterojunction diode, a novel stack for high-power and high-temperature electronic devices. This approach underscores the powerful synergy between LLMs and the development of instruments for scientific inquiry, showcasing a path for further acceleration in materials science.

翻译：大型语言模型（LLMs）正在改变化学与材料科学的研究格局。近期LLM加速实验研究的案例包括：用于从文献中解析合成配方的虚拟助手，或利用提取的知识指导合成与表征。尽管取得了这些进展，其应用仍局限于配备自动化仪器与控制软件的实验室，材料科学的大部分研究仍依赖人工操作。本文中，我们展示了使用ChatGPT-4为Keithley 2400电源测量单元快速部署基于Python的控制模块。通过迭代优化，我们以最少的人工干预实现了高效的仪器管理。此外，还创建了用户友好的图形用户界面（GUI），将全部仪器控制功能有效关联至交互式屏幕元素。最后，我们将此人工智能构建的仪器控制软件与高性能随机优化算法相集成，以促进从电流-电压（IV）测量数据中快速自动提取与半导体电荷传输机制相关的电子器件参数。该集成产生了一个基于IV曲线测量的、用于半导体器件表征与分析的完整开源工具包。我们通过采集、分析并参数化Pt/Cr$_2$O$_3$:Mg/$\beta$-Ga$_2$O$_3$异质结二极管（一种用于高功率高温电子器件的新型堆叠结构）的IV数据，演示了这些工具的应用。该方法凸显了LLMs与科学探究仪器开发之间的强大协同效应，为材料科学的进一步加速发展指明了一条路径。