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 13,878 pieces of structured
material knowledge data. This specialized AI model, named MatChat, focuses on
predicting inorganic material synthesis pathways. MatChat exhibits remarkable
proficiency in generating and reasoning with knowledge in materials science.
Although MatChat requires further refinement to meet the diverse material
design needs, this research undeniably highlights its impressive reasoning
capabilities and innovative potential in the field of materials science.
MatChat 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.

本研究利用 LLaMA2-7B 模型，结合 13,878 件结构化材料知识数据，通过学习过程增强了 MatChat AI 模型，以预测无机材料合成路径。尽管 MatChat 还需要进一步完善以满足不同的材料设计需求，但本研究无疑突出了其出色的推理能力和在材料科学领域的创新潜力，同时以开源方式提供了 MatChat 的模型和应用框架，为将生成式人工智能与材料科学整合的协作创新奠定了坚实的基础。