{Reference Type}: Journal Article
{Title}: Large-Language-Model-Based AI Agent for Organic Semiconductor Device Research.
{Author}: Zhang Q;Hu Y;Yan J;Zhang H;Xie X;Zhu J;Li H;Niu X;Li L;Sun Y;Hu W;
{Journal}: Adv Mater
{Volume}: 0
{Issue}: 0
{Year}: 2024 May 30
{Factor}: 32.086
{DOI}: 10.1002/adma.202405163
{Abstract}: Large language models (LLMs) have attracted widespread attention recently, however, their application in specialized scientific fields still requires deep adaptation. Here, an artificial intelligence (AI) agent for organic field-effect transistors (OFETs) is designed by integrating the generative pre-trained transformer 4 (GPT-4) model with well-trained machine learning (ML) algorithms. It can efficiently extract the experimental parameters of OFETs from scientific literature and reshape them into a structured database, achieving precision and recall rates both exceeding 92%. Combined with well-trained ML models, this AI agent can further provide targeted guidance and suggestions for device design. With prompt engineering and human-in-loop strategies, the agent extracts sufficient information of 709 OFETs from 277 research articles across different publishers and gathers them into a standardized database containing more than 10 000 device parameters. Using this database, a ML model based on Extreme Gradient Boosting is trained for device performance judgment. Combined with the interpretation of the high-precision model, the agent has provided a feasible optimization scheme that has tripled the charge transport properties of 2,6-diphenyldithieno[3,2-b:2',3'-d]thiophene OFETs. This work is an effective practice of LLMs in the field of organic optoelectronic devices and expands the research paradigm of organic optoelectronic materials and devices.