Autonomous Droplet Microfluidic Design Framework with Large Language Models.

ABSTRACT: Droplet-based microfluidic devices have substantial promise as cost-effective alternatives to current assessment tools in biological research. Moreover, machine learning models that leverage tabular data, including input design parameters and their corresponding efficiency outputs, are increasingly utilized to automate the design process of these devices and to predict their performance. However, these models fail to fully leverage the data presented in the tables, neglecting crucial contextual information, including column headings and their associated descriptions. This study presents μ-Fluidic-LLMs, a framework designed for processing and feature extraction, which effectively captures contextual information from tabular data formats. μ-Fluidic-LLMs overcomes processing challenges by tra

SUBMITTER: Nguyen DN 

PROVIDER: S-EPMC12508994 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature