  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.
