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The rapid advancement of artificial intelligence has transformed materials science research, with large language models (LLMs) emerging as a pivotal driver of innovation. This review explores the comprehensive role of LLMs in accelerating materials design across the entire research lifecycle, from knowledge mining to intelligent design. The study aims to highlight how LLMs can address challenges in traditional materials research, such as data fragmentation, high experimental costs, and limited reasoning capabilities, by leveraging their strengths in information retrieval, cross-modal data integration, and intelligent reasoning.
Key methodologies include the application of LLMs in knowledge discovery through techniques like retrieval-augmented generation (RAG), multi-modal information retrieval, and knowledge graph construction. These approaches enable efficient extraction and structuring of materials data from vast repositories of scientific literature and experimental records. Additionally, LLMs are integrated with automated experimental platforms to optimize workflows, from natural language-driven experiment design to high-throughput iterative testing.
The results demonstrate that LLMs significantly enhance materials research efficiency and accuracy. For instance, in knowledge mining, LLMs improve information retrieval precision by up to 29.4% in tasks like predicting material synthesis conditions. In materials design, LLMs enable accelerated computational modeling, structural and property prediction, and inverse design, reducing experimental trial-and-error cycles. Notably, LLMs excel in cross-scale knowledge integration, linking material composition, processing parameters, and performance metrics to guide innovative synthesis pathways.
However, challenges persist, including the reliance on high-quality data, the "black-box" nature of LLMs, and limitations in handling complex material systems. Future directions emphasize enhancing data quality through multi-source integration, improving model explainability via visualization tools, and deepening interdisciplinary collaboration to bridge gaps between AI and domain-specific expertise.
In conclusion, LLMs are reshaping materials science by enabling data-driven, knowledge-intensive research paradigms. Their ability to integrate vast datasets, predict material properties, and automate experimental workflows positions them as indispensable tools for accelerating materials discovery and innovation. As LLMs evolve, their synergy with physical constraints and experimental platforms promises to unlock new frontiers in materials design.-
Keywords:
- Large Language Models /
- Material Design /
- Intelligent Tasks /
- Synthesis and Property Prediction
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[1] Lewis P, Perez E, Piktus A, Petroni F, Karpukhin V, Goyal N, Kuttler H, Lewis M, Yin W, Rocktaschel T, Riedel S, Kiela D 2020 34th Conference on Neural Information Processing Systems (NeurIPS) Canada, December 6-12 2020, p16792
[2] Shi L, Liu Z M, Yang Y, Wu W Z, Zhang Y Y, Zhang H B,Lin J, Wu S Y, Chen Z H, Li R M, Wang N, Liu Z P, Tan H B, Gao H Y, Zhang Y, Wang G 2024 arXiv:2408.04665v2 [cs.CL]
[3] Luu R K., Buehler M J. 2024 Adv. Sci. 11 e2306724
[4] Li J T, Liu Y Q, Fan W Q, Wei X Y, Liu H, Tang J L, Li Q 2024 IEEE Trans. Knowl. Data Eng. 36 6071
[5] Park N H., Callahan T J., Hedrickd J L., Erdmann T, Capponi S 2024 arXiv:2408. 11793v2[cs.AI]
[6] Chiang Y, Hsieh E, Chou C H, Riebesell J 2024 arXiv:2401.17244v3[cs.CL]
[7] Tang Y H, Xu W B, Cao J, Ma J Z, Gao W L, Farrell S, Erichson B, Mahoney M W., Nonaka A, Yao Z 2025 arXiv:2502.13107v2[cs.AI]
[8] Li C T, Han X, Jiang R H, Yun P W, Hu P F, Ban X J 2023 J. Eng. Sci. 46 290（in Chinese）[李长泰, 韩旭, 蒋若辉, 贠培文, 胡鹏飞, 班晓娟 2023 工程科学学报 46 290]
[9] Tanishq G, Mohd Z, Krishnan N.M.A, Mausam 2022 npj Comput. Mater. 8 940
[10] Lai N S, Tew Y S, Zhong X L, Yin J, Li J L, Yan B H, Wang X N 2023 Ind. Eng. Chem. Res. 62 17835
[11] Thway M, Low A K. Y., Khetan S, Dai H W, Recatala J, Chen A P, Hippalgaonkar K 2024 Digit. Discov. 3 328
[12] Yu S L, Ran N, Liu J J 2024 Artif. Intell. Chem. 2 100076
[13] Durmaz A., Thomas A., Mishra L., Murthy R., Straub T. 2024 Sci. Data. 11 1
[14] Lei G, Docherty R, Cooper S J. 2024 Digit. Discov. 3 1257
[15] Weston L., Tshitoyan V., Dagdelen J., Kononova O., Trewartha A., Persson K. A., Ceder G. 2019 J. Chem. Inf. Model. 59 3692
[16] Zeng Z, Yin B C, Wang S P, Liu J R, Yang C, Yao H S, Sun X Z, Sun M S, Xie G T, Liu Z Y 2024 Bioinform. 40 btae534
[17] Jia X, Aziz A, Hashimoto Y, Li H 2024 Sci. China Mater. 67 1173
[18] Polak Maciej P., Morgan D. 2024 Nat. Commun. 15 1
[19] Foppiano L, Lambard G, Amagasa T, Ishii M 2024 Sci. Technol. Adv. Mater. Methods. 4 2356506
[20] Dagdelen J, Dunn A, Lee S, Walker N, Rosen A S., Ceder G, Persson K A., Jain A 2024 Nat. Commun. 15 1
[21] Zheng Z L, Zhang O F, Borgs C, Chayes J T., Yaghi O M. 2023 J. Am. Chem. Soc. 145 18048
[22] Yang J M, Walker K C., Bekar A A., Hao B, Bhadelia N, Joseph D, Paschalidis I Ch., 2024 Int. J. Med. Inform. 189 105500
[23] Shi Z B, Zhu L Y, Le X Q 2024 Data Anal. Knowl. Discov. 8 23（in Chinese）[时宗彬, 朱丽雅, 乐小虬 2024 数据分析与知识发现 8 23]
[24] Markus B J. 2023 J.jmps 181 105454
[25] Zia G A J, Valdestilhas A, Torres B J M, Kruschwitz S 2024 1st International Workshop on Semantic Materials Science: Harnessing the Power of Semantic Web Technologies in Materials Science, SeMatS 2024 Amsterdam, the Netherlands, September 17-19 2024, p101
[26] Corlatescu D G, Watanabe M, Ruseti S, Dascalu M, McNamara D S. 2024 Comput. Hum. Behav. 154 108154
[27] Bai X F, He S, Li Y, Xie Y B, Zhang X, Du W L, Li J R 2025 NPJ Comput. Mater. 11 1
[28] He Q, Yang X Q, Xu Y 2018 J. Reinf. Plast. Compos. 4 62（in Chinese）[贺强, 杨晓强, 徐艺 2018 玻璃钢／复合材料 4 62]
[29] Chen H L 2018 Building and Investigating a Metal Materials Image Knowledge Base Derived from Wikipedia M.S. Dissertation（Shijiazhuang: Hebei University of Science and Technology） (in Chinese) [陈慧琳 2018 基于Wikipedia金属材料图片知识库的构建与研究 硕士学位论文 (石家庄：河北科技大学)]
[30] Ye Y P, Ren J, Wang S Z, Wan Y W, Wang H F, Razzak I, Hoex B, Xie T, Zhang W J 2024 arXiv:2404.03080v3[cs.CL]
[31] Yang F L, Egon C, Xue J, Ryuhei S, Kazuaki K, Yusuke H, Shin O, Li H 2023 Nano Mater. Sci. 6 256
[32] Songshan Lake Materials Laboratory, Chinese Academy of Sciences Institute of Physics https://news.qq.com/rain/a/20250211A03D5O00 [2025-2-11]
[33] Buehler M J. 2024 ACS Eng. Au 4 241
[34] Cai J M, Yuan Y J, Sui X P, Lin Y Z, Zhuang K, Xu Y, Zhang Q, Ukrainczyk N, Xie T Y 2024 Constr. Build. Mater. 425 135965
[35] Ren H Y, Liu J P, Wang J, Gu X X, Chen X, Zhang Y, Zhao C X 2024 Comput. Eng. Appl. 12 24（in Chinese）[任海玉, 刘建平, 王健, 顾勋勋, 陈曦, 张越, 赵昌顼 2024 计算机工程与应用 12 24]
[36] Bran A M., Cox S, Schilter O, Baldassari C, White A D., Schwaller P 2024 Nat. Mach. Intell. 6 525
[37] Ansari M, Watchorn J, Brown C E., Brown J S. 2024 arXiv:2410. 03963v1[physics. chem-ph]
[38] Zheng Z L, Rampal N, Inizan T J, Borgs C, Chayes J T., Yaghi O M. 2025 Nat. Rev. Mater. 10 369
[39] Yoshikawa N, Skreta M, Darvish K, Arellano S, Zhi J, Kristensen L B, Li A Z, Zhao Y C, Xu H P, Kuramshin A, Aspuru A, Shkurti F, Garg A 2023 Auton. Robot. 47 1057
[40] Zhu Q, Zhang F, Huang Y, Xiao H Y, Zhao L Y, Zhang X C, Song T, Tang X S, Li X, He G, Chong B C, Zhou J Y, Zhang Y H, Zhang B C, Cao J Q, Luo M, Wang S, Ye G L, Zhang W J, Chen X, Cong S, Zhou D L, Li H R, Li J L, Zou G, Shang W W, Jiang J, Luo Y 2022 Natl. Sci. Rev. 9 nwac190
[41] Dai T, Vijayakrishnan S, Szczypiński F T., Ayme J F, Simaei E, Fellowes T, Clowes R, Kotopanov L, Shields C E., Zhou Z X, Ward J W., Cooper A I. 2024 Nat. 635 890
[42] Seifrid M, Strieth F, Haddadnia M, Wu T C., Alca E, Bodo L, Arellano S, Yoshikawa N, Skreta M, Keunen R, Aspuru A 2024 Digit. Discov. 3 1319
[43] Dr. Zheng Z L, Dr. Federico F, Brooke J, Wu H Y, Dr. Li S, Dr. Kakasaheb Y. N, Dr. Chase A. S, Dr. Jason G. M, Prof. William H. G, Prof. Klavs F. Jensen 2024 Angew. Chem. Int. Ed. 64 e202418074
[44] Ruan Y X, Lu C Y, Xu N, He Y C, Chen Y X, Zhang J, Xuan J, Pan J Z, Fang Q, Gao H Y, Shen X D, Ye N, Zhang Q, Mo Y M 2024 Nat. Commun. 15 1
[45] Hatakeyama K, Ishikawa H, Takaishi S, Igarashi Y, Nabae Y, Hayakawa T 2024 Polym. J. 56 997
[46] J Y Zhou, M Luo, L J Chen, Q Zhu, S Jiang, F Zhang, W W Shang, J Jiang 2025 Digit. Discov. 4 636
[47] Antunes L M., Butler K T., Grau C R 2024 Nat. Commun. 15 1
[48] Szymanski N J., Rendy B, Fei Y X, Kumar R E., He T J, Milsted D, McDermott M J., Gallant M, Cubuk E D, Merchant A, Kim H, Jain A, Bartel C J., Persson K, Zeng Y, Ceder G 2023 Nat. 624 86
[49] Sriram A, Miller B K, Chen R T.Q., Wood B M. 2024 arXiv:2410.23405v1[cs.LG]
[50] Jia S Y, Zhang C, Fung V 2024 arXiv:2406.13163v1[cond-mat. mtrl-sci]
[51] Liu H, Zheng H, Jia Z H, Zhou B H, Liu Y, Chen X L, Feng Y J, Li W, Yang W J, Li H 2023 Front. Chem. Sci. Eng. 17 2156
[52] Zhang C Y, Wang X Y, Wang Z Y 2024 Chin. J. Catal. 59 7
[53] Slautin B N., Liu Y T, Liu Y, Emery R, Hong S, Dubey A, Shvartsman V V., Lupascu D C., Sanchez S L., Ahmadi M, Kim Y, Strelcov E, Brown K A., Rack P D., Kalinin S V. 2025 arXiv:2501.02503v1[cond-mat.mtrl-sci]
[54] Su Y M, Wang X, Ye Y X, Xie Y B, Xu Y J, Jiang Y B, Wang C 2024 Chem. Sci. 15 12200
[55] Chen Z Y, Xie F K, Wan M, Yuan Y, Liu M, Wang Z G, Meng S, Wang Y L 2023 Chin. Phys. B 32 173（in Chinese）[陈子逸, 谢帆恺, 万萌, 袁扬, 刘淼, 王宗国, 孟胜, 王彦棡 2023 Chinese Physics B 32 173]
[56] Zhang D, Li H 2024 arXiv:10.26434[cs.DL]
[57] Razlivina J, Dmitrenko A, Vinogradov V 2024 J. Phys. Chem. 15 5804
[58] Chen X Q, Gao Y, Wang L D, Cui W J, Huang J M, Du Y, Wang B 2024 Sci. Data 11 1
[59] Liu S Y, Wen T Q, Pattamatta A. S. L. S, Srolovitz D J. 2024 Mater. Today 80 240
[60] Chen C, Maqsood A, Zhang Z, Wang X B, Duan L R, Wang H H, Chen T Y, Liu S Y, Li Q T, Luo J S, Jacobsson T. J 2024 Cell Rep. Phys. Sci. 5 102058
[61] Wang J Y, Liu X J, Wu Y, Wang H, Ma D, Lu Z P 2023 Acta Mater. 261 119386
[62] Wang Z W, Han M, Jin B 2024 Environ. Chem. 43 69（in Chinese）[王紫维, 韩民, 金彪 2024 环境化学 43 69]
[63] Ding R, Wang X B, Tan A, Li J, Liu J G 2023 ACS Catal. 13 13267
[64] Unni R, Zhou M Y, Wiecha P R., Zheng Y B 2024 Curr. Opin. Solid State Mater. Sci. 30 101157
[65] Han X Q,Wang X D, Xu M Y, Feng Z, Yao B W, Guo P J, Gao Z F, Lu Z Y 2025 Chin. Phys. Lett. 42 135
[66] Choudhary K 2024 J. Am. Chem. Soc. 15 6909
[67] Oliveira J, O. N. Christino, L. Oliveira M. C. F., Paulovich F. V. 2023 J. Chem. Inf. Model. 63 7605
[68] Liu G,Sun M, Wojciech M, Jiang M, Chen J 2024 arXiv:2410. 04223v1[cs.LG]
[69] Zhang C W, Zhai Y S, Gong Z Y, Duan H L, She Y B, Yang Y F, Su A 2024 J. Cheminform. 16 1
[70] Choi J, Lee B 2024 Commun. Mater. 5 1
[71] Wang Y X, Li Y, Tang Z C, Li H, Yuan Z L, Tao H G, Zou N L, Bao T, Liang X H, Chen Z Z, Xu S H, Bian C, Xu Z M, Wang C, Si C, Duan W H, Xu Y 2024 Sci. Bull. 69 2514
[72] Liu Y, Yang Z W, Yu Z Y, Liu Z T, Liu D H, Lin H L, Li M Q, Ma S C, Avdeev X, Shi S Q 2023 J. Materiomics 9 798
[73] Markus J. B 2024 Adv. Funct. Mater. 34 9531
[74] Wu T, Shen J Z, Jia Z X, Wang Y X, Zheng Z L 2025 arXiv:2502. 18890v1[cs.CL]
[75] Obuchi K, Funaya K, Toyama K 2024 NEC Tech. J. 17 46
[76] Li R C, Patel T, Wang Q Y, Du X Y 2024 arXiv:2408. 14033v2 [cs.AI]
[77] Luo Z M, Yang Z L, Xu Z X, Yang W, Du X Y 2025 arXiv:2501. 04306v1[cs.CL]
[78] Tang Z C, Li H, Lin P Z, Gong X X, Jin G, He L X, Jiang H, Ren X G, Duan W H, Xu Y 2024 Nat. Commun. 15 8815
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