用于材料发现的生成式人工智能——无需理解的设计

Jianjun Hu, Qin Li, Nihang Fu

工程(英文) ›› 2024, Vol. 39 ›› Issue (8) : 13-17.

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工程(英文) ›› 2024, Vol. 39 ›› Issue (8) : 13-17. DOI: 10.1016/j.eng.2024.07.008
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用于材料发现的生成式人工智能——无需理解的设计

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Generative AI for Materials Discovery: Design Without Understanding

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Jianjun Hu, Qin Li, Nihang Fu. 用于材料发现的生成式人工智能. Engineering. 2024, 39(8): 13-17 https://doi.org/10.1016/j.eng.2024.07.008

参考文献

[1]
McCormack J, Dorin A, Innocent T. Generative design:a paradigm for design research. In: RedmondJ, DurlingD, deBono A, editors. Proceedingsof Futureground, DesignResearch Society International Conference; Nov 17-21 ; Melbourne VIC, 2004 Australia. Clayton: Monash University Publishing; 2004..
[2]
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al.. Generative adversarial networks. Commun ACM, 63 (11) (2020), pp. 139-144.
[3]
C. Bilodeau, W. Jin, T. Jaakkola, R. Barzilay, K.F. Jensen. Generative models for molecular discovery: recent advances and challenges. Wiley Interdiscip Rev Comput Mol Sci, 12 (5) (2022), p. e1608.
[4]
L. Regenwetter, A.H. Nobari, F. Ahmed. Deep generative models in engineering design: a review. J Mech Des, 144 (7) (2022), Article 071704.
[5]
E. Bengio, M. Jain, M. Korablyov, D. Precup, Y. Bengio. Flow network based generative models for non-iterative diverse candidate generation. M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, J. Wortman Vaughan (Eds.), Advances in neural information processing systems 34, Curran Associates, Inc., Red Hook (2021), pp. 27381-27394.
[6]
D.W. Davies, K.T. Butler, A.J. Jackson, A. Morris, J.M. Frost, J.M. Skelton, et al.. Computational screening of all stoichiometric inorganic materials. Chem, 1 (4) (2016), pp. 617-627.
[7]
Sawada Y, Morikawa K, Fujii M. Study of deep generative models for inorganic chemical compositions. 2019. arXiv:1910.11499..
[8]
Y. Dan, Y. Zhao, X. Li, S. Li, M. Hu, J. Hu. Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. npj Comput Mater, 6 (1) (2020), p. 84.
[9]
N. Fu, L. Wei, Y. Song, Q. Li, R. Xin, S.S. Omee, et al.. Material transformers: deep learning language models for generative materials design. Mach Learn Sci Technol, 4 (1) (2023), Article 015001.
[10]
Wei L, Li Q, Song Y, Stefanov S, Siriwardane EMD, Chen F, et al. Crystal transformer: self-learning neural language model for generative and tinkering design of materials. 2022. arXiv:2204.11953..
[11]
S. Kim, J. Noh, G.H. Gu, A. Aspuru-Guzik, Y. Jung. Generative adversarial networks for crystal structure prediction. ACS Cent Sci, 6 (8) (2020), pp. 1412-1420.
[12]
C.J. Court, B. Yildirim, A. Jain, J.M. Cole. 3-D inorganic crystal structure generation and property prediction via representation learning. J Chem Inf Model, 60 (10) (2020), pp. 4518-4535.
[13]
Y. Zhao, M. Al-Fahdi, M. Hu, E.M.D. Siriwardane, Y. Song, A. Nasiri, et al.. High-throughput discovery of novel cubic crystal materials using deep generative neural networks. Adv Sci, 8 (20) (2021), Article 2100566.
[14]
Y. Zhao, E.M.D. Siriwardane, Z. Wu, N. Fu, M. Al-Fahdi, M. Hu, et al.. Physics guided deep learning for generative design of crystal materials with symmetry constraints. npj Comput Mater, 9 (1) (2023), p. 38.
[15]
Xie T, Fu X, Ganea OE, Barzilay R, Jaakkola T. Crystal diffusion variational autoencoder for periodic material generation. 2021. arXiv:2110.06197..
[16]
Zeni C, Pinsler R, Zügner D, Fowler A, Horton M, Fu X, et al. MatterGen: a generative model for inorganic materials design. 2023. arXiv:2312.03687..
[17]
M.J. Buehler. Generative pretrained autoregressive transformer graph neural network applied to the analysis and discovery of novel proteins. J Appl Phys, 134 (8) (2023), Article 084902.
[18]
R.K. Luu, M. Wysokowski, M.J. Buehler. Generative discovery of de novo chemical designs using diffusion modeling and transformer deep neural networks with application to deep eutectic solvents. Appl Phys Lett, 122 (23) (2023), Article 234103.
[19]
A.J. Lew, M.J. Buehler. Single-shot forward and inverse hierarchical architected materials design for nonlinear mechanical properties using an attention-diffusion model. Mater Today, 64 (2023), pp. 10-20.
[20]
B. Ni, D.L. Kaplan, M.J. Buehler. ForceGen: end-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci Adv, 10 (6) (2024), Article eadl4000.
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