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The prediction of chemical synthesis pathways plays a pivotal role inmaterials science research. Challenges, such as the complexity of synthesispathways and the lack of comprehensive datasets, currently hinder our abilityto predict these chemical processes accurately. However, recent advancements ingenerative artificial intelligence (GAI), including automated text generationand question-answering systems, coupled with fine-tuning techniques, havefacilitated the deployment of large-scale AI models tailored to specificdomains. In this study, we harness the power of the LLaMA2-7B model and enhanceit through a learning process that incorporates 13,878 pieces of structuredmaterial knowledge data. This specialized AI model, named MatChat, focuses onpredicting inorganic material synthesis pathways. MatChat exhibits remarkableproficiency in generating and reasoning with knowledge in materials science.Although MatChat requires further refinement to meet the diverse materialdesign needs, this research undeniably highlights its impressive reasoningcapabilities and innovative potential in the field of materials science.MatChat is now accessible online and open for use, with both the model and itsapplication framework available as open source. This study establishes a robustfoundation for collaborative innovation in the integration of generative AI inmaterials science.

MatChat: A Large Language Model and Application Service Platform for Materials Science