From text to test: AI-generated control software for materials science instruments†

Abstract

Large language models (LLMs) are one of the AI technologies that 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. However, these AI-driven materials advances are limited to a few laboratories with existing automated instruments and control software, whereas the rest of materials science research remains highly manual. AI-crafted control code for automating scientific instruments would democratize and further accelerate materials research advances, but reports of such AI applications remain scarce. The goal of this manuscript is to demonstrate how to swiftly establish a Python-based control module for a scientific measurement instrument solely through interactions with ChatGPT-4. Through a series of test and correction cycles, we achieved successful management of a common Keithley 2400 electrical source measure unit instrument with minimal human-corrected code, and discussed lessons learned from this development approach for scientific software. Additionally, a user-friendly graphical user interface (GUI) was created, effectively linking all instrument controls to interactive screen elements, and text prompts as well as JSON templates for interaction with ChatGPT are provided for this and other instruments. 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/Cr₂O₃:Mg/β-Ga₂O₃ 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 to further accelerate research progress towards synthesis and characterization in materials science.