摘要
The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artificial intelligence(GAI), including automated text generation and question–answering systems, coupled with fine-tuning techniques, have facilitated the deployment of large-scale AI models tailored to specific domains. In this study, we harness the power of the LLaMA2-7B model and enhance it through a learning process that incorporates 13878 pieces of structured material knowledge data.This specialized AI model, named Mat Chat, focuses on predicting inorganic material synthesis pathways. Mat Chat exhibits remarkable proficiency in generating and reasoning with knowledge in materials science. Although Mat Chat requires further refinement to meet the diverse material design needs, this research undeniably highlights its impressive reasoning capabilities and innovative potential in materials science. Mat Chat is now accessible online and open for use, with both the model and its application framework available as open source. This study establishes a robust foundation for collaborative innovation in the integration of generative AI in materials science.
作者
陈子逸
谢帆恺
万萌
袁扬
刘淼
王宗国
孟胜
王彦棡
Zi-Yi Chen;Fan-Kai Xie;Meng Wan;Yang Yuan;Miao Liu;Zong-Guo Wang;Sheng Meng;Yan-Gang Wang(Computer Network Information Center,Chinese Academy of Sciences,Beijing 100083,China;University of Chinese Academy of Sciences,Beijing 100049,China;Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China;School of Physical Sciences,University of Chinese Academy of Sciences,Beijing 100190,China;Songshan Lake Materials Laboratory,Dongguan 523808,China;Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing 100049,China)
基金
supported by the Informatization Plan of the Chinese Academy of Sciences (Grant No. CASWX2023SF-0101)
the Key Research Program of Frontier Sciences, CAS (Grant No. ZDBS-LY-7025)
the Youth Innovation Promotion Association CAS (Grant No. 2021167)
the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB33020000)。
