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240531s2024 xx |||||o 00| ||eng c |
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|a 10.1002/adma.202405163
|2 doi
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|a pubmed24n1495.xml
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|a (DE-627)NLM37304285X
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|a (NLM)38816034
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|a DE-627
|b ger
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Zhang, Qian
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Large-Language-Model-Based AI Agent for Organic Semiconductor Device Research
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|c 2024
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Revised 08.08.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 Wiley‐VCH GmbH.
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|a 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
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4 |
|a Journal Article
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| 650 |
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4 |
|a accelerated design
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| 650 |
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4 |
|a large language models
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| 650 |
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4 |
|a machine learning
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| 650 |
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4 |
|a organic field‐effect transistors
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| 700 |
1 |
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|a Hu, Yongxu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yan, Jiaxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Hengyue
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xie, Xinyi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhu, Jie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Huchao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Niu, Xinxin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Li, Liqiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sun, Yajing
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Hu, Wenping
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Advanced materials (Deerfield Beach, Fla.)
|d 1998
|g 36(2024), 32 vom: 05. Aug., Seite e2405163
|w (DE-627)NLM098206397
|x 1521-4095
|7 nnns
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| 773 |
1 |
8 |
|g volume:36
|g year:2024
|g number:32
|g day:05
|g month:08
|g pages:e2405163
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| 856 |
4 |
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|u http://dx.doi.org/10.1002/adma.202405163
|3 Volltext
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