Large‐Language‐Model‐Based AI Agent for Organic Semiconductor Device Research

Abstract

Large language models (LLMs) have attracted widespread attention recently, however, their application in specialized scientific fields still requires deep adaptation. In this work, we designed an artificial intelligence (AI) agent for organic field effect transistors (OFETs) by integrating the 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 extracted sufficient information of 709 OFETs from 277 research articles across different publishers and gathered them into a standardized database containing more than 10000 device parameters. Using this database, we trained a ML model based on XGBoost 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 DP‐DTT OFETs. Our 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.This article is protected by copyright. All rights reserved