MatChat: A large language model and application service platform for materials science

doi:10.1088/1674-1056/ad04cb

中国物理B ›› 2023, Vol. 32 ›› Issue (11): 118104-118104.doi: 10.1088/1674-1056/ad04cb

所属专题： Featured Column — COMPUTATIONAL PROGRAMS FOR PHYSICS

MatChat: A large language model and application service platform for materials science

Zi-Yi Chen(陈子逸)^1,2,†, Fan-Kai Xie(谢帆恺)^3,4,†, Meng Wan(万萌)^1,†, Yang Yuan(袁扬)^1,2, Miao Liu(刘淼)^3,5,6,‡, Zong-Guo Wang(王宗国)^1,2,§, Sheng Meng(孟胜)^3,5, and Yan-Gang Wang(王彦棡)^1,2

1 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
4 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China;
5 Songshan Lake Materials Laboratory, Dongguan 523808, China;
6 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

收稿日期:2023-10-11 修回日期:2023-10-18 接受日期:2023-10-19 出版日期:2023-10-16 发布日期:2023-11-03
通讯作者: Miao Liu, Zong-Guo Wang E-mail:mliu@iphy.ac.cn;wangzg@cnic.cn
基金资助:
This work was 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), and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB33020000).

MatChat: A large language model and application service platform for materials science

1 Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China;
2 University of Chinese Academy of Sciences, Beijing 100049, China;
3 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;
4 School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China;
5 Songshan Lake Materials Laboratory, Dongguan 523808, China;
6 Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2023-10-11 Revised:2023-10-18 Accepted:2023-10-19 Online:2023-10-16 Published:2023-11-03
Contact: Miao Liu, Zong-Guo Wang E-mail:mliu@iphy.ac.cn;wangzg@cnic.cn
Supported by:
This work was 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), and the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDB33020000).

摘要/Abstract

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 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.

关键词: MatChat, materials science, generative artificial intelligence

Abstract: 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.

Key words: MatChat, materials science, generative artificial intelligence

中图分类号: (New materials: theory, design, and fabrication)

81.05.Zx

01.50.hv (Computer software and software reviews) 81.16.Be (Chemical synthesis methods)

Zi-Yi Chen(陈子逸), Fan-Kai Xie(谢帆恺), Meng Wan(万萌), Yang Yuan(袁扬), Miao Liu(刘淼), Zong-Guo Wang(王宗国), Sheng Meng(孟胜), and Yan-Gang Wang(王彦棡). MatChat: A large language model and application service platform for materials science[J]. 中国物理B, 2023, 32(11): 118104-118104.

参考文献

[1] OpenAI 2023 arXiv:2303.08774[cs.CL]
[2] Du Z X, Qian Y J, Liu X, Ding M, Qiu J Z, Yang Z L and Tang J 2022 Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers) (Dublin, Association for Computational Linguistics) pp. 320-335
[3] Zeng A H, Liu X, Du Z X, Wang Z H, Lai H Y, Ding M, Yang Z Y, Xu Y F, Zheng W D, Xia X, Weng L T, Ma Z X, Xue Y F, Zhai J D, Chen W G, Liu Z Y, Zhang P, Dong Y X and Tang J 2022 arXiv:2210.02414[cs.CL]
[4] Sun Y, Wang S H, Li Y K, Feng S K, Chen X Y, Zhang H, Tian X, Zhu D X, Tian H and Wu H 2019 arXiv:1904.09223[cs.CL]
[5] Sun Y, Wang S H, Li Y K, Feng S K, Tian H, Wu H and Wang H F 2020 The Thirty-Fourth AAAI Conference on Artificial Intelligence (California:AAAI Press, Palo Alto) pp. 8968-8975
[6] Sun Y, Wang S H, Feng S K, Ding S Y, Pang C, Shang J Y, Liu J Y, Chen X Y, Zhang H, Zhao Y B, Lu Y X, Liu W X, Wu Z H, Gong W B, Liang J Z, Shang Z Z, Sun P, Liu W, Ouyang X, Yu D H, Tian H, etc. 2021 arXiv:2107.02137[cs.CL]
[7] Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M A, Lacroix T, Roziére B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A, Grave E and Lample G 2023 arXiv:2302.13971[cs.CL]
[8] Touvron H, Martin L, Stone K, Albert P, Almahairi A, Babaei Y, Bashlykov N, Batra S, Bhargava P, Bhosale S, Bikel D, Blecher L, Ferrer C C, Chen M Y, Cucurull G, Esiobu D, Fernandes J, Fu J, Fu W Y and Fuller B 2023 arXiv:2307.09288[cs.CL]
[9] Yang P, Wang J J, Gan R Y, Zhu X Y, Zhang L, Wu Z W, Gao X Y, Zhang J X and Sakai T 2022 arXiv:2210.08590[cs.CL]
[10] Zhang H B, Chen J Y, Jiang F, Yu F, Chen Z H, Li J Q, Chen G M, Wu X B, Zhang Z Y, Xiao Q Y, Wan X, Wang B Y and Li H Z 2023 arXiv:2305.15075[cs.CL]
[11] Xiong H L, Wang S, Zhu Y T, Zhao Z H, Liu Y X, Wang Q and Shen D G 2023 arXiv:2304.01097[cs.CL]
[12] Zhang X Y, Yang Q and Xu D L 2023 arXiv:2305.12002[cs.CL]
[13] Dan Y H, Lei Z K, Gu Y Y, Li Y, Yin J H, Lin J J, Ye L H, Tie Z Y, Zhou Y G, Wang Y L, Zhou A M, Zhou Z, Chen Q, Zhou J, He L and Qiu X P 2023 arXiv:2308.02773[cs.CL]
[14] Wang J J, Zhang Y X, Zhang L, Yang P, Gao X Y, Wu Z W, Dong X Q, and He J Q, Zhuo J H, Yang Q, Huang Y F, Li X Y, Wu, Y H, Lu J Y, Zhu X Y, Chen W F, Han T, Pan K H, Wang R, Wang H, et al. 2022 CoRR abs/2209.02970
[15] Xie F K, Lu T L, Yu Z, Wang Y X, Wang Z G, Meng S and Liu M 2023 Chin. Phys. Lett. 40 057401
[16] Xie F K, Lu T L, Yu Z, Wang Y X, Wang Z G, Meng S and Liu M 2023 Chin. Phys. Lett. 40 117101
[17] Jiang Y T, Yu Z, Wang Y X, Lu T L, Meng S, Jiang K, and Liu M. 2022 Chin. Phys. Lett. 39 047402
[18] Cheng Z and Yu Z H 2021 Chin. Phys. Lett. 38 070302
[19] Bai S C, Tang Y C and Ran S J 2022 Chin. Phys. Lett. 39 100701
[20] Ren H B, Wang L and Dai X 2021 Chin. Phys. Lett. 38 050701
[21] Jia H X, Horton M, Wang Y N, Zhang S J, Persson K A, Meng S and Liu M 2022 Adv. Sci. 9 2202756
[22] Liu M and Meng S 2023 Scientia Sinica Chimica 53 19
[23] Saal J E, Kirklin S, Aykol M, Meredig B and Wolverton C 2013 JOM 65 1501
[24] Jain A, Ong S P, Hautier G, Chen W, Richards W D, Dacek S, Cholia S, Gunter D, Skinner D, Ceder G and Persson K A 2013 APL Mater. 1 011002
[25] Liang Y Z, Chen M W, Wang Y N, Jia H X, Lu T L, Xie F K, Cai G H, Wang Z G, Meng S and Liu M 2023 Sci. China. Mater. 66 343
[26] Liu Z W, Guo J L, Chen Z Y, Wang Z G, Sun Z N, Li X W and Wang Y G 2022 Comp. Mater. Sci. 214 111699
[27] Guo J L, Chen Z Y, Liu Z W, Li X W, Xie Z Y, Wang Z G and Wang Y G 2022 Sci. Rep. 12 15326
[28] Gupta T, Zaki M, Krishnan N A and Mausam 2022 npj Comput. Mater. 8 102
[29] Devlin J, Chang M W, Lee K and Toutanova K 2019 Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Volume 1 (Long and Short Papers) (Minneapolis:Association for Computational Linguistics) pp. 4171-4186
[30] Wang Z R, Kononova O, Cruse K, He T J, Huo H Y, Fei Y X, Zeng Y, Sun Y Z, Cai Z J, Sun W H and Ceder G 2022 Sci. Data 9 231
[31] Hu Edward J., Shen Y L, Phillip Wallis, Allen-Zhu Z Y, Li Y Z, Wang S A, Wang L and Chen W Z 2021 arXiv:2106.09685[cs.CL]
[32] Lucacel R C, Ponta O, Licarete, E, Radu T and Simon V 2016 J. Non-Crystalline Solids 439 67
[33] Annapurna K, Dwivedi RN, Kundu P and Buddhudu S 2003 Mater. Lett. 57 2095

MatChat: A large language model and application service platform for materials science

MatChat: A large language model and application service platform for materials science

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	Qianwen Wang(汪倩文), Jixuan Wu(武继璇), Xuepeng Zhan(詹学鹏),Pengpeng Sang(桑鹏鹏), and Jiezhi Chen(陈杰智). P-type cold-source field-effect transistors with TcX₂ and ReX₂ (X=S, Se) cold source electrodes: A computational study[J]. 中国物理B, 2023, 32(12): 127203-127203.
[2]	Shenshen Yan(闫申申), Yan Liu(刘岩), Zi Wang(王子), Xiaohua Lan(兰晓华), Yi Wang(汪毅), and Jie Ren(任捷). Designing radiative cooling metamaterials for passive thermal management by particle swarm optimization[J]. 中国物理B, 2023, 32(5): 57802-057802.
[3]	Mao Liu(刘帽) and Quan Yan(严泉). Guide and control of thermal conduction with isotropic thermodynamic parameters based on a rotary-concentrating device[J]. 中国物理B, 2023, 32(4): 44402-044402.
[4]	Hung N M, Oanh L T M, Chung D P, Thang D V, Mai V T, Hang L T, and Minh N V. Tuning the particle size, physical properties, and photocatalytic activity of Ag₃PO₄ materials by changing the Ag⁺/PO₄^3- ratio[J]. 中国物理B, 2023, 32(3): 38102-038102.
[5]	Hui Chen(陈辉), Bin Hu(胡彬), Yuhan Ye(耶郁晗), Haitao Yang(杨海涛), and Hong-Jun Gao(高鸿钧). Superconductivity and unconventional density waves in vanadium-based kagome materials AV₃Sb₅[J]. 中国物理B, 2022, 31(9): 97405-097405.
[6]	Bin Wang(王斌) and Jiping Huang (黄吉平). Hydrodynamic metamaterials for flow manipulation: Functions and prospects[J]. 中国物理B, 2022, 31(9): 98101-098101.
[7]	Haotian Jiang(蒋浩天), Xing Xu(徐兴), Chao Fan(樊超), Beibei Dai(代贝贝), Zhuodong Qi(亓卓栋), Sha Jiang(蒋莎), Mengqiu Cai(蔡孟秋), and Qinglin Zhang(张清林). Edge assisted epitaxy of CsPbBr₃ nanoplates on Bi₂O₂Se nanosheets for enhanced photoresponse[J]. 中国物理B, 2022, 31(4): 48102-048102.
[8]	Meng Ma(马梦), Xiao-Qin Zhou(周晓勤), Hao Liu(刘浩), and Hao-Cheng Wang(王浩成). Negative compressibility property in hinging open-cell Kelvin structure[J]. 中国物理B, 2021, 30(5): 56201-056201.
[9]	. [J]. 中国物理B, 2021, 30(3): 38102-.
[10]	. [J]. 中国物理B, 2021, 30(1): 18105-.
[11]	Qian-Xing Chen(陈前行), Hao Yang(杨浩), Gang Chen(陈刚). [J]. 中国物理B, 2020, 29(10): 108103-.
[12]	陈孝章, 顾乐华, 刘岚, 陈华威, 栗敬俣, 刘春森, 周鹏. Temperature-switching logic in MoS₂ single transistors[J]. 中国物理B, 2020, 29(9): 97201-097201.
[13]	王爱伟, 刘子媛, 潘金波, 李乔楚, 李更, 郇庆, 杜世萱, 高鸿钧. Construction of monolayer IrTe₂ and the structural transition under low temperatures[J]. 中国物理B, 2020, 29(7): 78102-078102.
[14]	钱凯, 高蕾, 李航, 张帅, 严佳浩, 刘晨, 王嘉鸥, 钱天, 丁洪, 张余洋, 林晓, 杜世萱, 高鸿钧. Epitaxial growth and air-stability of monolayer Cu₂Te[J]. 中国物理B, 2020, 29(1): 18104-018104.
[15]	贺雨晴, 蒋毅, 张田田, 黄荷, 方辰, 金钟. SymTopo:An automatic tool for calculating topological properties of nonmagnetic crystalline materials[J]. 中国物理B, 2019, 28(8): 87102-087102.