DropMicroFluidAgents (DMFAs): autonomous droplet microfluidic research framework through large language model agents

Abstract

Large language models (LLMs) have gained significant attention in recent years due to their impressive capabilities across various tasks, from natural language understanding to generation. Applying LLMs within specific domains requires substantial adaptation to account for the unique terminologies, nuances, and context-specific challenges inherent to those areas. Here, we introduce DropMicroFluidAgents (DMFAs) employing LLM agents to perform two key functions: (1) delivering focused guidance, answers, and suggestions specific to droplet microfluidics and (2) generating machine learning models to optimise and automate the design of droplet microfluidic devices, including the creation of code-based computer-aided design (CAD) scripts to enable rapid and precise design execution. To assess the accuracy of DMFAs in question–answering tasks, we compiled a dataset of questions with corresponding ground-truth answers and established an evaluation criterion. Experimental evaluations demonstrated that integrating DMFAs with the LLAMA3.1 model yielded the highest accuracy of 76.15%, underscoring the significant performance enhancement provided by agent integration. This effect was particularly pronounced when DMFAs were paired with the GEMMA2 model, resulting in a 34.47% improvement in accuracy compared to the standalone GEMMA2 configuration. For evaluating the performance of DMFAs in design automation, we utilized an existing dataset on flow-focusing droplet microfluidics. The resulting machine learning model demonstrated a coefficient of determination of approximately 0.96. To enhance usability, we developed a streamlined graphical user interface (GUI) that offers an intuitive and effective means for users to interact with the system. This study demonstrates the effective use of LLM agents in droplet microfluidics research as powerful tools for automating workflows, synthesising knowledge, optimising designs, and interacting with external systems, bringing a significant transformation to the field of digital discovery. DMFAs is capable of transforming them into closed-loop digital discovery platforms that encompass literature synthesis, hypothesis generation, autonomous design, execution in self-driving laboratories, analysis of results, and the generation of new hypotheses. These capabilities enable their application across education and industrial support, driving greater efficiency in scientific discovery and innovation.