Citation: Zhilong Song, Shuaihua Lu, Qionghua Zhou, Jinlan Wang. T2MAT (text-to-material): A universal agent for generating material structures with goal properties from a single sentence[J]. Acta Physico-Chimica Sinica, ;2026, 42(5): 100213. doi: 10.1016/j.actphy.2025.100213 shu

T2MAT (text-to-material): A universal agent for generating material structures with goal properties from a single sentence

Zhilong Song ^{1, 2} ,
Shuaihua Lu ¹ ,
Qionghua Zhou ^{1, 2,,} ,
Jinlan Wang ^{1, 2,,}

1.
Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, Jiangsu Province, China
2.
Suzhou Laboratory, Suzhou 215004, Jiangsu Province, China

Corresponding author: Qionghua Zhou, qh.zhou@seu.edu.cn Jinlan Wang, jlwang@seu.edu.cn
Received Date: 25 August 2025
Revised Date: 27 October 2025
Accepted Date: 27 October 2025

Artificial Intelligence-Generated Content (AIGC)—content autonomously produced by AI systems without human intervention—has significantly boosted efficiency across various fields. However, AIGC in material science faces challenges in efficiently discovering novel materials that surpass existing databases, while ensuring the invariance and stability of crystal structures. To address these challenges, we develop T2MAT (text-to-material), an end-to-end agent that transforms user-input text into the inverse design of novel material structures with target properties beyond existing database, enabled by comprehensive exploration of chemical space and fully automated first-principles validation. Furthermore, we propose CGTNet (Crystal Graph Transformer NETwork), a graph neural network specifically designed to capture long-range interactions, which dramatically improves the accuracy and data efficiency of property predictions and thereby strengthens the reliability of inverse design. Through these contributions, T2MAT reduces the reliance on human expertise and accelerates the discovery of high-performance functional materials, paving the way for truly autonomous material design.
- Agent,
- Material design,
- Large language model,
- Generative model,
- Graph neural network
1. [1]
  Y. Cao, S. Li, Y. Liu, Z. Yan, Y. Dai, P.S. Yu, L. Sun, arXiv: 2303.04226, http://arxiv.org/abs/2303.04226.
2. [2]
  Editorial, Nat. Mach. Intell. 4 (2022) 733, https://doi.org/10.1038/s42256-022-00539-8. doi: 10.1038/s42256-022-00539-8
3. [3]
  K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Nature 559 (2018) 547, https://doi.org/10.1038/s41586-018-0337-2. doi: 10.1038/s41586-018-0337-2
4. [4]
  M. Zhong, K. Tran, Y. Min, C. Wang, Z. Wang, C.T. Dinh, P. De Luna, Z. Yu, A.S. Rasouli, P. Brodersen, et al., Nature 581 (2020) 178, https://doi.org/10.1038/s41586-020-2242-8. doi: 10.1038/s41586-020-2242-8
5. [5]
  B. Weng, Z. Song, R. Zhu, Q. Yan, Q. Sun, C.G. Grice, Y. Yan, W.-J. Yin, Nat. Commun. 11 (2020) 3513, https://doi.org/10.1038/s41467-020-17263-9. doi: 10.1038/s41467-020-17263-9
6. [6]
  Z. Song, X. Wang, F. Liu, Q. Zhou, W.-J. Yin, H. Wu, W. Deng, J. Wang, Mater. Horizons 10 (2023) 1651, https://doi.org/10.1039/D3MH00157A. doi: 10.1039/D3MH00157A
7. [7]
  S. Lu, Q. Zhou, Y. Ouyang, Y. Guo, Q. Li, J. Wang, Nat. Commun. 9 (2018) 3405, https://doi.org/10.1038/s41467-018-05761-w. doi: 10.1038/s41467-018-05761-w
8. [8]
  S. Lu, Q. Zhou, Y. Guo, J. Wang, Chem 8 (2022) 769, https://doi.org/10.1016/j.chempr.2021.11.009. doi: 10.1016/j.chempr.2021.11.009
9. [9]
  B. Sanchez-Lengeling, A. Aspuru-Guzik, Science 361 (2018) 360, https://doi.org/10.1126/science.aat2663. doi: 10.1126/science.aat2663
10. [10]
  J. Noh, G.H. Gu, S. Kim, Y. Jung, Chem. Sci. 11 (2020) 4871, https://doi.org/10.1039/d0sc00594k. doi: 10.1039/d0sc00594k
11. [11]
  S. Lu, Q. Zhou, X. Chen, Z. Song, J. Wang, Natl. Sci. Rev. 9 (2022) 9, https://doi.org/10.1093/nsr/nwac111. doi: 10.1093/nsr/nwac111
12. [12]
  Y. Zhao, M. Al‐Fahdi, M. Hu, E.M.D. Siriwardane, Y. Song, A. Nasiri, J. Hu, Adv. Sci. 8 (2021) 14, https://doi.org/10.1002/advs.202100566. doi: 10.1002/advs.202100566
13. [13]
  Z. Yao, B. Sánchez-Lengeling, N.S. Bobbitt, B.J. Bucior, S.G.H. Kumar, S.P. Collins, T. Burns, T.K. Woo, O.K. Farha, R.Q. Snurr, et al., Nat. Mach. Intell. 3 (2021) 76, https://doi.org/10.1038/s42256-020-00271-1. doi: 10.1038/s42256-020-00271-1
14. [14]
  B. Kim, S. Lee, J. Kim, Sci. Adv. 6 (2020) eaax9324, https://doi.org/10.1126/sciadv.aax9324. doi: 10.1126/sciadv.aax9324
15. [15]
  T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, Adv. Neural Inf. Process. Syst. (2016) 2234, https://dl.acm.org/doi/10.5555/3157096.3157346 doi: 10.5555/3157096.3157346
16. [16]
  K.M. Jablonka, P. Schwaller, A. Ortega-Guerrero, B. Smit, Nat. Mach. Intell. 6 (2024) 161, https://doi.org/10.1038/s42256-023-00788-1. doi: 10.1038/s42256-023-00788-1
17. [17]
  L.M. Antunes, K.T. Butler, R. Grau-Crespo, Nat. Coummn. (2024) 10570, https://doi.org/10.1038/s41467-024-54639-7. doi: 10.1038/s41467-024-54639-7
18. [18]
  T. Xie, J.C. Grossman, Phys. Rev. Lett. 120 (2018) 145301, https://doi.org/10.1103/PhysRevLett.120.145301. doi: 10.1103/PhysRevLett.120.145301
19. [19]
  J. Gasteiger, J. Groß, S. Günnemann, arXiv: 2003.03123, https://arxiv.org/abs/2003.03123.
20. [20]
  K.T. Schütt, H.E. Sauceda, P.-J. Kindermans, A. Tkatchenko, K.-R. Müller, J. Chem. Phys. 148 (2018) 241722, https://doi.org/10.1063/1.5019779. doi: 10.1063/1.5019779
21. [21]
  M. Shuaibi, A. Kolluru, A. Das, A. Grover, A. Sriram, Z. Ulissi, C.L. Zitnick, arXiv: 2106.09575, http://arxiv.org/abs/2106.09575.
22. [22]
  J. Gasteiger, F. Becker, S. Günnemann, GemNet: Universal Directional Graph Neural Networks for Molecules, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021), Curran Associates, Inc., 2021, pp. 6790–6802, https://proceedings.neurips.cc/paper/2021/hash/35cf8659cfcb13224cbd47863a34fc58-Abstract.html.
23. [23]
  K.T. Schütt, O.T. Unke, M. Gastegger, Equivariant Message Passing for the Prediction of Tensorial Properties and Molecular Spectra, in Proceedings of the 38th International Conference on Machine Learning (ICML 2021), PMLR, 2021, pp. 9377–9388, https://proceedings.mlr.press/v139/schutt21a.html.
24. [24]
  Y.-L. Liao, B. Wood, A. Das, T. Smidt, EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations, in International Conference on Learning Representations (ICLR), 2024, https://proceedings.iclr.cc/paper_files/paper/2024/file/ab12e8f3443c1a789f595b18d8c597b4-Paper-Conference.pdf.
25. [25]
  M. Fey, J.E. Lenssen, Fast Graph Representation Learning with PyTorch Geometric, in ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019, https://rlgm.github.io/papers/2.pdf.
26. [26]
  Z. Song, S. Lu, M. Ju, Q. Zhou, J. Wang, Nat. Commun. 16 (2025) 6530, https://doi.org/10.1038/s41467-025-61778-y. doi: 10.1038/s41467-025-61778-y
27. [27]
  D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Bi, et al., Nature 645 (2025) 633, https://doi.org/10.1038/s41586-025-09422-z. doi: 10.1038/s41586-025-09422-z
28. [28]
  H. Touvron, L. Martin, K. Stone, P. Albert, A. Almahairi, Y. Babaei, N. Bashlykov, S. Batra, P. Bhargava, S. Bhosale, et al., arXiv: 2307.09288, http://arxiv.org/abs/2307.09288
29. [29]
  Z. Du, Y. Qian, X. Liu, M. Ding, J. Qiu, Z. Yang, J. Tang, Proc. Annu. Meet. Assoc. Comput. Linguist. 1 (2022) 320, https://doi.org/10.18653/v1/2022.acl-long.26. doi: 10.18653/v1/2022.acl-long.26
30. [30]
  T.B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., Language Models are Few-Shot Learners, in Advances in Neural Information Processing Systems 33 (NeurIPS 2020), Curran Associates, Inc., 2020, pp. 1877–1901, https://papers.nips.cc/paper/2020/hash/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html
31. [31]
  S.P. Ong, W.D. Richards, A. Jain, G. Hautier, M. Kocher, S. Cholia, D. Gunter, V.L. Chevrier, K.A. Persson, G. Ceder, Comput. Mater. Sci. 68 (2013) 314, https://doi.org/10.1016/j.commatsci.2012.10.028. doi: 10.1016/j.commatsci.2012.10.028
32. [32]
  S. Haastrup, M. Strange, M. Pandey, T. Deilmann, P.S. Schmidt, N.F. Hinsche, M.N. Gjerding, D. Torelli, P.M. Larsen, A.C. Riis-Jensen, et al., 2D Mater. 5 (2018) 042002, https://doi.org/10.1088/2053-1583/aacfc1. doi: 10.1088/2053-1583/aacfc1
33. [33]
  M.N. Gjerding, A. Taghizadeh, A. Rasmussen, S. Ali, F. Bertoldo, T. Deilmann, N.R. Knøsgaard, M. Kruse, A.H. Larsen, S. Manti, et al., 2D Mater. 8 (2021) 044002, https://doi.org/10.1088/2053-1583/ac1059. doi: 10.1088/2053-1583/ac1059
34. [34]
  A.S. Rosen, S.M. Iyer, D. Ray, Z. Yao, A. Aspuru-Guzik, L. Gagliardi, J.M. Notestein, R.Q. Snurr, Matter 4 (2021) 1578, https://doi.org/10.1016/j.matt.2021.02.015. doi: 10.1016/j.matt.2021.02.015
35. [35]
  A.S. Rosen, V. Fung, P. Huck, C.T. O'Donnell, M.K. Horton, D.G. Truhlar, K.A. Persson, J.M. Notestein, R.Q. Snurr, npj Comput. Mater. 8 (2022) 112, https://doi.org/10.1038/s41524-022-00796-6. doi: 10.1038/s41524-022-00796-6
36. [36]
  T. Xie, X. Fu, O.-E. Ganea, R. Barzilay, T. Jaakkola, arXiv: 2110.06197, https://arxiv.org/abs/2110.06197.
37. [37]
  R. Jiao, W. Huang, Y. Liu, D. Zhao, Y. Liu, arXiv: 2402.03992, https://arxiv.org/abs/2402.03992.
38. [38]
  B.K. Miller, R.T.Q. Chen, A. Sriram, B.M. Wood. FlowMM: Generating Materials with Riemannian Flow Matching, in Proceedings of the 41st International Conference on Machine Learning, PMLR, 2024, pp. 35664–35686, https://proceedings.mlr.press/v235/miller24a.html.
39. [39]
  C. Zeni, R. Pinsler, D. Zügner, A. Fowler, M. Horton, X. Fu, Z. Wang, A. Shysheya, J. Crabbé, S. Ueda, et al., Nature 639 (2025) 624, https://doi.org/10.1038/s41586-025-08628-5. doi: 10.1038/s41586-025-08628-5
40. [40]
  Z. Song, C. Ling, Q. Li, Q. Zhou, J. Wang, arXiv: 2507.19307, http://arxiv.org/abs/2507.19307.
41. [41]
  A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin. Attention is All you Need, in Advances in Neural Information Processing Systems 30 (NIPS 2017), Curran Associates, Inc., 2017, https://papers.neurips.cc/paper/7181-attention-is-all-you-need.pdf.
42. [42]
  J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Association for Computational Linguistics: Minneapolis, Minnesota, 2019, pp. 4171–4186, https://aclanthology.org/N19-1423/.
43. [43]
  H. Huo, M. Rupp, Mach. Learn. Sci. Technol. 3 (2022) 045017, https://doi.org/10.1088/2632-2153/aca005. doi: 10.1088/2632-2153/aca005
44. [44]
  L. Yu, A. Zunger, Phys. Rev. Lett. 108 (2012) 068701, https://doi.org/10.1103/PhysRevLett.108.068701. doi: 10.1103/PhysRevLett.108.068701
45. [45]
  S. Kong, F. Ricci, D. Guevarra, J. B. Neaton, C. P. Gomes, J. M. Gregoire, Nat. Commun. 13 (2022) 949, https://doi.org/10.1038/s41467-022-28543-x. doi: 10.1038/s41467-022-28543-x
46. [46]
  C. Loh, T. Christensen, R. Dangovski, S. Kim, M. Soljačić, Nat. Commun. 13 (2022) 4223, https://doi.org/10.1038/s41467-022-31915-y. doi: 10.1038/s41467-022-31915-y
47. [47]
  S. Kirklin, J.E. Saal, B. Meredig, A. Thompson, J.W. Doak, M. Aykol, S. Rühl, C. Wolverton, npj Comput. Mater. 1 (2015) 15010, https://doi.org/10.1038/npjcompumats.2015.10. doi: 10.1038/npjcompumats.2015.10
48. [48]
  K. Choudhary, K.F. Garrity, A.C.E. Reid, B. DeCost, A.J. Biacchi, A.R. Hight Walker, Z. Trautt, J. Hattrick-Simpers, A.G. Kusne, A. Centrone, et al., npj Comput. Mater. 6 (2020) 173, https://doi.org/10.1038/s41524-020-00440-1. doi: 10.1038/s41524-020-00440-1
49. [49]
  Z. Yang, D. Bourgeois, J. You, M. Zitnik, J. Leskovec, GNNExplainer: Generating Explanations for Graph Neural Networks, in Advances in Neural Information Processing Systems 32 (NuerIPS 2019), Curran Associates, Inc., 2019, https://proceedings.neurips.cc/paper_files/paper/2019/file/d80b7040b773199015de6d3b4293c8ff-Paper.pdf.
50. [50]
  W.-J. Yin, T. Shi, Y. Yan, Adv. Mater. 26 (2014) 4653, https://doi.org/10.1002/adma.201306281. doi: 10.1002/adma.201306281
51. [51]
  W.-J. J. Yin, T. Shi, Y. Yan, Appl. Phys. Lett. 104 (2014) 063903, https://doi.org/10.1063/1.4864778. doi: 10.1063/1.4864778
52. [52]
  S.M. Lundberg, S.I. Lee, A Unified Approach to Interpreting Model Predictions, in Advances in Neural Information Processing Systems 30 (NIPS 2017), Curran Associates, Inc., 2017, https://proceedings.neurips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf.
53. [53]
  K. Momma, F. Izumi, J. Appl. Crystallogr. 44 (2011) 1272, https://doi.org/10.1107/S0021889811038970. doi: 10.1107/S0021889811038970
54. [54]
  G. Kresse, J. Furthmüller, Phys. Rev. B 54 (1996) 11169, https://doi.org/10.1103/PhysRevB.54.11169. doi: 10.1103/PhysRevB.54.11169
55. [55]
  P. Giannozzi, S. Baroni, N. Bonini, M. Calandra, R. Car, C. Cavazzoni, D. Ceresoli, G. L. Chiarotti, M. Cococcioni, I. Dabo, et al., J. Phys. Condens. Matter 21 (2009) 395502, https://doi.org/10.1088/0953-8984/21/39/395502. doi: 10.1088/0953-8984/21/39/395502
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