Toward Precise, Multiscale, and Process-Aware Catalysis Generation via Generative AI

Wentao Li; Leyi Zhao; Jiangjie Qiu; Zemeng Wang; Yijun Li; Manu Suvarna; Qilong Cai; Jinxing Chen; Jiong Lu; Xiaonan Wang

doi:10.1016/j.eng.2026.07.027

Engineering ›› :202607027 DOI: 10.1016/j.eng.2026.07.027

research-article

Toward Precise, Multiscale, and Process-Aware Catalysis Generation via Generative AI

Author information +

History +

PDF (2256KB)

Abstract

Cite this article

Download citation ▾

Wentao Li, Leyi Zhao, Jiangjie Qiu, Zemeng Wang, Yijun Li, Manu Suvarna, Qilong Cai, Jinxing Chen, Jiong Lu, Xiaonan Wang. Toward Precise, Multiscale, and Process-Aware Catalysis Generation via Generative AI. Engineering 202607027 DOI:10.1016/j.eng.2026.07.027

登录浏览全文

4963

注册一个新账户忘记密码

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	Vogt C, Weckhuysen BM . The concept of active site in heterogeneous catalysis. Nat Rev Chem 2022; 6(2):89-111.

[2]	Suvarna M, Pérez—Ramírez J . Embracing data science in catalysis research. Nat Catal 2024; 7(6):624—35.

[3]	Benavides—Hernández J, Dumeignil F . From characterization to discovery: artificial intelligence, machine learning and high—throughput experiments for heterogeneous catalyst design. ACS Catal 2024; 14(15):11749-79.

[4]	De Breuck PP, Wang HC, Rignanese GM, Botti S, Marques MAL . Generative AI for crystal structures: a review. npj Comput Mater 2025; 11(1):370.

[5]

Jiao R, Huang W, Lin P, Han J, Chen P, Lu Y, et al. Crystal structure prediction by joint equivariant diffusion. In: Oh A, Naumann T, Globerson A, Saenko K, Hardt M, Levine S, editors. Proceedings of the 37th International Conference on Neural Information Processing Systems; 2023 Dec 10—16; New Orleans, LA, USA. Red Hook: Curran Associates Inc.; 2023; p. 17464—97.

[6]	Zeni C, Pinsler R, Zügner D, Fowler A, Horton M, Fu X, et al. A generative model for inorganic materials design. Nature 2025; 639(8055):624—32.

[7]

Miller BK, Chen RTQ, Sriram A, Wood BM. FlowMM: generating materials with Riemannian flow matching. In: Salakhutdinov R, Kolter Z, Heller K, Weller A, Oliver N, Scarlett J, et al., editors. Proceedings of the 41st International Conference on Machine Learning; 2024 Jul 21—27; Vienna, Austria. New York City: Association of Computing Machinery; 2024. p. 35664—86.

[8]	Luo X, Wang Z, Wang Q, Shao X, Lv J, Wang L, et al. CrystalFlow: a flow—based generative model for crystalline materials. Nat Commun 2025; 16:9267.

[9]	Song Z, Fan L, Lu S, Ling C, Zhou Q, Wang J . Inverse design of promising electrocatalysts for CO₂ reduction via generative models and bird swarm algorithm. Nat Commun 2025; 16:1053.

[10]	Mok DH, Back S . Generative pretrained transformer for heterogeneous catalysts. J Am Chem Soc 2024; 146(49):33712—22.

[11]	Yin J, Li W, Chen H, Qiu J, Feng H, Xu X, et al. CaTS: toward scalable and efficient transition state screening for catalyst discovery. ACS Catal 2025; 15(18):15754-64.

[12]

Kolluru A, Kitchin JR. AdsorbDiff: adsorbate placement via conditional denoising diffusion. In: Salakhutdinov R, Kolter Z, Heller K, Weller A, Oliver N, Scarlett J, et al., editors. Proceedings of the 41st International Conference on Machine Learning; 2024 Jul 21—27; Vienna, Austria. New York City: Association of Computing Machinery; 2024, p. 25042—57.

[13]	Qiu J, Li W, Chen H, Zhao L, Wang X . AdsorbFlow: energy—conditioned flow matching enables fast and realistic adsorbate placement. 2026. arXiv:2602.19289.

[14]	Yu Y, Wu B, Wei R, Ren N, You S . Visualizing nexus of porous architecture and reactive transport in heterogeneous catalysis by deep learning computer vision and transfer learning. Nat Commun 2025; 16:8107.

[15]	Son YW, Pak JH, Kim C, Lee JM . Generative transformer—based deep hierarchical VAE model for the automated generation of chemical process topologies. Comput Chem Eng 2026; 205(Pt 1):109431.