Matchat: A large language model and application service platform for materials science
Open accessAbstract
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 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 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.
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In science, some existing works design LLM-based agents with a large amount of knowledge in memory to solve problems.
“In the domain of science, some existing works design LLM-based agents with a large amount of knowledge in memory to solve problems [158, 160, 162].”
From A Survey on the Memory Mechanism of Large Language Model-based Agents (2025) · cites this paper as [162]
The Materials knowledge data section describes fine-tuning an LLM on 13,878 synthesis-pathway descriptions, and the Conclusion says MatChat uses that knowledge to reason about materials synthesis questions. The paper describes a model and dialogue platform, not an LLM-based agent with a large knowledge store in memory. Its in-memory Redis storage, described in Architecture and method design, is for token verification rather than materials knowledge. (LLM, conf=0.84)
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Cited by
Sorted by citations
A Survey on the Memory Mechanism of Large Language Model-based Agents
Zhang, Z., Dai, Q., Bo, X., Ma, C., Li, R., Chen, X., Zhu, J., Dong, Z., & Wen, J.-R. (2025) · ACM Transactions on Information Systems · doi:10.1145/3748302 · Cited by 118 · see citing works
- Authors
- Chen 陈, Z.-Y. 子., Xie 谢, F.-K. 帆., Wan 万, M. 萌., Yuan 袁, Y. 扬., Liu 刘, M. 淼., Wang 王, Z.-G. 宗., Meng 孟, S. 胜., & Wang 王, Y.-G. 彦.
- Venue
- Chinese Physics B
- Publisher
- IOP Publishing
- Published
- 2023-11-01
- Volume
- 32
- Issue
- 11
- Pages
- 118104
- ISSN
- 1674-1056 (ISSN-L)
- 2058-3834
- OpenAlex
- W4387779638
- Cited by
- 40
- References
- 0
- Citations by year
- 2026: 17 · 2025: 15 · 2024: 8
- FWCI
- 2.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Chinese Physics B (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Chinese Academy of Sciences (CAS- WX2023SF-0101, XDB33020000)
- Youth Innovation Promotion Association of the Chinese Academy of Sciences (2021167)
- Youth Innovation Promotion Association (2021167)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, materials design
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