Intelligent chemical synthesis based on microchemical engineering technology

doi:10.1016/j.cjche.2025.05.010

中国化学工程学报 ›› 2025, Vol. 84 ›› Issue (8): 274-288.DOI: 10.1016/j.cjche.2025.05.010

• Review • 上一篇

Intelligent chemical synthesis based on microchemical engineering technology

Yongqi Pan¹, Yazi Yu², Lijie Wang³, Guogang Hu³, Yujun Wang¹, Guangsheng Luo¹

1. State Key Laboratory of Chemical Engineering and Low-Carbon Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;
2. College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China;
3. North China Pharmaceutical Hebei Huamin Pharmaceutical Co., Ltd, Shijiazhuang 052165, China

收稿日期:2025-01-14 修回日期:2025-04-16 接受日期:2025-05-06 出版日期:2025-08-28 发布日期:2025-06-04
通讯作者: Yujun Wang,E-mail:wangyujun@mail.tsinghua.edu.cn
基金资助:
This work was financially supported by the National Natural Science Foundation of China (22378227) and Shijiazhuang Science and Technology Bureau (231790163A).

Intelligent chemical synthesis based on microchemical engineering technology

Yongqi Pan¹, Yazi Yu², Lijie Wang³, Guogang Hu³, Yujun Wang¹, Guangsheng Luo¹

1. State Key Laboratory of Chemical Engineering and Low-Carbon Technology, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China;
2. College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China;
3. North China Pharmaceutical Hebei Huamin Pharmaceutical Co., Ltd, Shijiazhuang 052165, China

Received:2025-01-14 Revised:2025-04-16 Accepted:2025-05-06 Online:2025-08-28 Published:2025-06-04
Contact: Yujun Wang,E-mail:wangyujun@mail.tsinghua.edu.cn
Supported by:
This work was financially supported by the National Natural Science Foundation of China (22378227) and Shijiazhuang Science and Technology Bureau (231790163A).

摘要/Abstract

摘要： Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions; however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.

关键词: Flow chemistry, Microreactor, Microfluidics, Machine learning, Multi-phase disperse

Abstract: Chemical synthesis is essential in industries such as petrochemicals, fine chemicals, and pharmaceuticals, driving economic and social development. The increasing demand for new molecules and materials calls for novel chemical reactions; however, manual experimental screening is time-consuming. Artificial intelligence (AI) offers a promising solution by leveraging large-scale experimental data to model chemical reactions, although challenges such as the lack of standardization and predictability in chemical synthesis hinder AI applications. Additionally, the multi-scale nature of chemical reactions, along with complex multiphase processes, further complicates the task. Recent advances in microchemical systems, particularly continuous flow methods using microreactors, provide precise control over reaction conditions, enhancing reproducibility and enabling high-throughput experimentation. These systems minimize transport-related inconsistencies and facilitate scalable industrial applications. This review systematically explores recent developments in intelligent synthesis based on microchemical systems, focusing on reaction system design, synthesis robots, closed-loop optimization, and high-throughput experimentation, while identifying key areas for future research.

Key words: Flow chemistry, Microreactor, Microfluidics, Machine learning, Multi-phase disperse

Yongqi Pan, Yazi Yu, Lijie Wang, Guogang Hu, Yujun Wang, Guangsheng Luo. Intelligent chemical synthesis based on microchemical engineering technology[J]. 中国化学工程学报, 2025, 84(8): 274-288.

Yongqi Pan, Yazi Yu, Lijie Wang, Guogang Hu, Yujun Wang, Guangsheng Luo. Intelligent chemical synthesis based on microchemical engineering technology[J]. Chinese Journal of Chemical Engineering, 2025, 84(8): 274-288.

参考文献

[1] Q.Q. Xue, B.H. Yan, Y.J. Wang, G.S. Luo, Continuous synthesis of atomically dispersed Rh supported on MgAl2O₄ using two-stage microreactor, AlChE. J. 68 (11) (2022) e17841.
[2] Q. Chen, S.T. Xia, Y.J. Wang, G.S. Luo, H.Y. Shang, K. Wang, Continuous synthesis of 1-ethoxy-2, 3-difluoro-4-iodo-benzene in a microreactor system and the Gaussian and computational fluid dynamics simulations, AlChE. J. 67 (6) (2021) e17217.
[3] S.T. Xia, X.F. Ding, Y.J. Wang, G.S. Luo, Z.T. Wu, Y.F. Cheng, B.Y. Hao, Large-scale synthesis of dihydrostreptomycin via hydrogenation of streptomycin in a membrane dispersion microreactor, Chem. Eng. J. 334 (2018) 2250-2254.
[4] S.T. Xia, X.F. Ding, Y.J. Wang, G.S. Luo, Continuous-flow synthesis of an important liquid-crystal intermediate using a microreaction system, Ind. Eng. Chem. Res. 57 (36) (2018) 12113-12121.
[5] S. Urgaonkar, J. Verkade, Palladium/proazaphosphatrane-catalyzed amination of aryl halides possessing a phenol, alcohol, acetanilide, amide or an enolizable ketone functional group: Efficacy of lithium bis(trimethylsilyl)amide as the base, Adv. Synth. Catal. 346 (6) (2004) 611-616.
[6] D.J. Koza, Y.A. Nsiah, Palladium catalyzed C-N bond formation in the synthesis of 7-amino-substituted tetracyclines, J. Org. Chem. 67 (14) (2002) 5025-5027.
[7] C.W. Coley, W.H. Green, K.F. Jensen, Machine learning in computer-aided synthesis planning, Acc. Chem. Res. 51 (5) (2018) 1281-1289.
[8] J.A. Keith, V. Vassilev-Galindo, B.Q. Cheng, S. Chmiela, M. Gastegger, K.R. Muller, A. Tkatchenko, Combining machine learning and computational chemistry for predictive insights into chemical systems, Chem. Rev. 121 (16) (2021) 9816-9872.
[9] H.X. Mai, T.C. Le, D.H. Chen, D.A. Winkler, R.A. Caruso, Machine learning for electrocatalyst and photocatalyst design and discovery, Chem. Rev. 122 (16) (2022) 13478-13515.
[10] M. Christensen, L.P.E. Yunker, P. Shiri, T. Zepel, P.L. Prieto, S. Grunert, F. Bork, J.E. Hein, Automation isn’t automatic, Chem. Sci. 12 (47) (2021) 15473-15490.
[11] E.N. Muratov, J. Bajorath, R.P. Sheridan, I.V. Tetko, D. Filimonov, V. Poroikov, T.I. Oprea, I.I. Baskin, A. Varnek, A. Roitberg, O. Isayev, S. Curtarolo, D. Fourches, Y. Cohen, A. Aspuru-Guzik, D.A. Winkler, D. Agrafiotis, A. Cherkasov, A. Tropsha, QSAR without borders, Chem. Soc. Rev. 49 (11) (2020) 3525-3564.
[12] K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh, Machine learning for molecular and materials science, Nature 559 (7715) (2018) 547-555.
[13] X. Hong, Q. Yang, K.B. Liao, J.F. Pei, M. Chen, F.Y. Mo, H. Lu, W.B. Zhang, H.S. Zhou, J.X. Chen, L.B. Su, S.Q. Zhang, S.Y. Liu, X. Huang, Y.Z. Sun, Y.X. Wang, Z.X. Zhang, Z.Z. Yu, S.Z. Luo, X.F. Fu, S.L. You, AI for organic and polymer synthesis, Sci. China Chem. 67 (8) (2024) 2461-2496.
[14] M. Christensen, Y. Xu, E.E. Kwan, M.J. Di Maso, Y. Ji, M. Reibarkh, A.C. Sun, A. Liaw, P.S. Fier, S. Grosser, J.E. Hein, Dynamic sampling in autonomous process optimization, Chem. Sci. 15 (19) (2024) 7160-7169.
[15] M. Christensen, L.P.E. Yunker, F. Adedeji, F. Hase, L.M. Roch, T. Gensch, G. Dos Passos Gomes, T. Zepel, M.S. Sigman, A. Aspuru-Guzik, J.E. Hein, Data-science driven autonomous process optimization, Commun. Chem. 4 (1) (2021) 112.
[16] E. King-Smith, S. Berritt, L. Bernier, X.J. Hou, J.L. Klug-McLeod, J. Mustakis, N.W. Sach, J.W. Tucker, Q.Y. Yang, R.M. Howard, A.A. Lee, Probing the chemical ‘reactome’ with high-throughput experimentation data, Nat. Chem. 16 (4) (2024) 633-643.
[17] J.T. Rapp, B.J. Bremer, P.A. Romero, Self-driving laboratories to autonomously navigate the protein fitness landscape, Nat. Chem. Eng. 1 (1) (2024) 97-107.
[18] S.W. Li, L.C. Xu, C. Zhang, S.Q. Zhang, X. Hong, Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge, Nat. Commun. 14 (1) (2023) 3569.
[19] Q. Zhu, F. Zhang, Y. Huang, H.Y. Xiao, L.Y. Zhao, X.C. Zhang, T. Song, X.S. Tang, X. Li, G. He, B.C. Chong, J.Y. Zhou, Y.H. Zhang, B.C. Zhang, J.Q. Cao, M. Luo, S. Wang, G.L. Ye, W.J. Zhang, X. Chen, S. Cong, D.L. Zhou, H.R. Li, J.L. Li, G. Zou, W.W. Shang, J. Jiang, Y. Luo, An all-round AI-chemist with a scientific mind, Natl. Sci. Rev. 9 (10) (2022) nwac190.
[20] N. Gesmundo, K. Dykstra, J.L. Douthwaite, Y.T. Kao, R.H. Zhao, B. Mahjour, R. Ferguson, S. Dreher, B. Sauvagnat, J. Sauri, T. Cernak, Miniaturization of popular reactions from the medicinal chemists’ toolbox for ultrahigh-throughput experimentation, Nat. Synth. 2 (2023) 1082-1091.
[21] H.T. Zhao, W. Chen, H. Huang, Z.H. Sun, Z.J. Chen, L.J. Wu, B.C. Zhang, F.M. Lai, Z. Wang, M.L. Adam, C.H. Pang, P.K. Chu, Y. Lu, T. Wu, J. Jiang, Z.Y. Yin, X.F. Yu, A robotic platform for the synthesis of colloidal nanocrystals, Nat. Synth. 2 (2023) 505-514.
[22] D.T. Ahneman, J.G. Estrada, S.S. Lin, S.D. Dreher, A.G. Doyle, Predicting reaction performance in C-N cross-coupling using machine learning, Science 360 (6385) (2018) 186-190.
[23] Q. Chen, S. Ullah, Y.J. Wang, G.S. Luo, Controllable generation of multi-row bubbly flow in coupled microstructures and its boosting of photocatalytic decarboxylative carbonylation, Chem. Eng. J. 464 (2023) 142758.
[24] Q. Chen, Y.J. Wang, G.S. Luo, Visible-light-driven direct decarboxylative carbonylation of carboxylic acids using acridine photocatalysis in oxygen-liquid flow, Chem. Eng. J. 461 (2023) 141767.
[25] Z.Y. Yu, C.Y. Guo, X.M. Pang, Y.G. Shen, M.T. Gao, S.Y. Zhao, Y.J. Wang, G.S. Luo, Coprecipitation synthesis of large-pore-volume γ-alumina nanofibers by two serial membrane dispersion microreactors with a circulating continuous phase, Ind. Eng. Chem. Res. 62 (3) (2023) 1415-1424.
[26] M.Z. Guo, S.Q. Bai, Y.J. Wang, G.S. Luo, Novel microfabricated nozzle array with grooves for microdroplet generation, Chem. Eng. J. 416 (2021) 129103.
[27] L. Sheng, L. Ma, Y.C. Chen, J. Deng, G.S. Luo, A comprehensive study of droplet formation in a capillary embedded step T-junction: From squeezing to jetting, Chem. Eng. J. 427 (2022) 132067.
[28] M. Pathak, Numerical simulation of membrane emulsification: Effect of flow properties in the transition from dripping to jetting, J. Membr. Sci. 382 (1-2) (2011) 166-176.
[29] P.M. Korczyk, V. van Steijn, S. Blonski, D. Zaremba, D.A. Beattie, P. Garstecki, Accounting for corner flow unifies the understanding of droplet formation in microfluidic channels, Nat. Commun. 10 (2019) 2528.
[30] S. Ullah, Y.Q. Pan, Q.Q. Xue, T.Y. Huang, Y.Z. Hu, R.P. Ye, Y.J. Wang, G.S. Luo, The structure activity relationship of promoted La doped Ni-CeO₂ catalysts prepared by continuous-flow microreactor for low temperature CO₂ methanation, Fuel 379 (2025) 133034.
[31] X.P. Luo, L.H. Du, F. He, C.H. Zhou, Controllable regioselective acylation of flavonoids catalyzed by lipase in microreactors, J. Carbohydr. Chem. 32 (7) (2013) 450-462.
[32] Q.L. Xu, G.S. Li, F.C. Zhu, N.D. Chen, C.W. Chen, Z.Q. Yu, Continuous synthesis of N-(3-Amino-4-methylphenyl)benzamide and its kinetics study in microflow system, J. Flow Chem. 12 (3) (2022) 317-325.
[33] H.L. Yi, S.L. Lu, J.J. Wu, Y.J. Wang, G.S. Luo, Parallelized microfluidic droplet generators with improved ladder-tree distributors for production of monodisperse γ-Al₂O₃ microspheres, Particuology 62 (2022) 47-54.
[34] F.Y. Zhao, R.H. Yang, J.X. Ma, Y. Gao, Y.J. Wang, G.S. Luo, Alumina microspheres for the adsorption of fatty alcohols containing oxygenates in Fischer-Tropsch synthetic oils, Sep. Purif. Technol. 326 (2023) 124593.
[35] J.R. Naber, S.L. Buchwald, Packed-bed reactors for continuous-flow C-N cross-coupling, Angew. Chem. Int. Ed 49 (49) (2010) 9469-9474.
[36] M. Chen, S.L. Buchwald, Continuous-flow synthesis of 1-substituted benzotriazoles from chloronitrobenzenes and amines in a C-N bond formation/hydrogenation/diazotization/cyclization sequence, Angew. Chem. Int. Ed 52 (15) (2013) 4247-4250.
[37] S. Roesner, S.L. Buchwald, Continuous-flow synthesis of biaryls by negishi cross-coupling of fluoro- and trifluoromethyl-substituted (hetero)arenes, Angew. Chem. Int. Ed 55 (35) (2016) 10463-10467.
[38] T. Noel, S.L. Buchwald, Cross-coupling in flow, Chem. Soc. Rev. 40 (10) (2011) 5010-5029.
[39] W.C. Fu, T.F. Jamison, Modular continuous flow synthesis of imatinib and analogues, Org. Lett. 21 (15) (2019) 6112-6116.
[40] J. Vicente, I. Saura-Llamas, M.J. Oliva-Madrid, J.A. Garcia-Lopez, D. Bautista, A new method for high-yield cyclopalladation of primary and secondary amines. atom-efficient open-to-air inexpensive synthesis of Buchwald-type precatalysts, Organometallics 30 (17) (2011) 4624-4631.
[41] P. Yaseneva, P. Hodgson, J. Zakrzewski, S. Falss, R.E. Meadows, A.A. Lapkin, Continuous flow Buchwald-Hartwig amination of a pharmaceutical intermediate, React. Chem. Eng. 1 (2) (2016) 229-238.
[42] S. Ullah, T.Y. Huang, Y.Q. Pan, Q.Q. Xue, Z.Y. Yu, Y.Z. Hu, S.M. Ahmed, R.P. Ye, Y.J. Wang, G.S. Luo, Ceria-modified high pore volume Ni/Al₂O₃ spheres for enhanced low-temperature CO₂ methanation, Fuel 390 (2025) 134736.
[43] G.M. Whitesides, The origins and the future of microfluidics, Nature 442 (7101) (2006) 368-373.
[44] C.W. Coley, D.A. Thomas 3rd, J.A.M. Lummiss, J.N. Jaworski, C.P. Breen, V. Schultz, T. Hart, J.S. Fishman, L. Rogers, H.Y. Gao, R.W. Hicklin, P.P. Plehiers, J. Byington, J.S. Piotti, W.H. Green, A. John Hart, T.F. Jamison, K.F. Jensen, A robotic platform for flow synthesis of organic compounds informed by AI planning, Science 365 (6453) (2019) eaax1566.
[45] Y. Xie, G.M. Huang, Y.J. Wang, Z.R. Yan, X. Wang, J. Huang, M.T. Gao, W.Y. Fei, G.S. Luo, Synthesis of piperacillin with low impurity content using a new three-feed membrane dispersion microreactor, Chem. Eng. J. 387 (2020) 124178.
[46] H.C. Shi, Y.M. Shi, Z.Q. Liang, S.L. Zhao, B. Qiao, Z. Xu, L.J. Wang, D.D. Song, Machine learning-enabled discovery of multi-resonance TADF molecules: Unraveling PLQY predictions from molecular structures, Chem. Eng. J. 494 (2024) 153150.
[47] H.X. Guo, G.S. Jiang, B.W. Diao, J.J. Du, W. Sun, J.L. Fan, X.J. Peng, An integrated screening approach for designing efficient thermally activated delayed fluorescent materials for OLEDs, J. Mater. Chem. C 12 (36) (2024) 14515-14522.
[48] R. Gomez-Bombarelli, J. Aguilera-Iparraguirre, T.D. Hirzel, D. Duvenaud, D. Maclaurin, M.A. Blood-Forsythe, H.S. Chae, M. Einzinger, D.G. Ha, T. Wu, G. Markopoulos, S. Jeon, H. Kang, H. Miyazaki, M. Numata, S. Kim, W. Huang, S.I. Hong, M. Baldo, R.P. Adams, A.. Aspuru-Guzik, Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach, Nat. Mater. 15 (10) (2016) 1120-1127.
[49] B.P. MacLeod, F.G.L. Parlane, C.C. Rupnow, K.E. Dettelbach, M.S. Elliott, T.D. Morrissey, T.H. Haley, O. Proskurin, M.B. Rooney, N. Taherimakhsousi, D.J. Dvorak, H.N. Chiu, C.E.B. Waizenegger, K. Ocean, M. Mokhtari, C.P. Berlinguette, A self-driving laboratory advances the Pareto front for material properties, Nat. Commun. 13 (1) (2022) 995.
[50] S.D. Xu, J.L. Li, P.F. Cai, X.L. Liu, B. Liu, X.N. Wang, Self-improving photosensitizer discovery system via Bayesian search with first-principle simulations, J. Am. Chem. Soc. 143 (47) (2021) 19769-19777.
[51] J.K. Sun, R. Tu, Y.C. Xu, H.Y. Yang, T. Yu, D. Zhai, X.Q. Ci, W.Q. Deng, Machine learning aided design of single-atom alloy catalysts for methane cracking, Nat. Commun. 15 (1) (2024) 6036.
[52] M. Zhong, K. Tran, Y.M. Min, C.H. Wang, Z.Y. Wang, C.T. Dinh, P. De Luna, Z.Q. Yu, A.S. Rasouli, P. Brodersen, S. Sun, O. Voznyy, C.S. Tan, M. Askerka, F.L. Che, M. Liu, A. Seifitokaldani, Y.J. Pang, S.C. Lo, A. Ip, Z. Ulissi, E.H. Sargent, Accelerated discovery of CO₂ electrocatalysts using active machine learning, Nature 581 (7807) (2020) 178-183.
[53] X. Song, P.X. Pu, H.S. Feng, H. Ding, Y. Deng, Z. Ge, S.Q. Zhao, T.Y. Liu, Y.S. Yang, M. Wei, X. Zhang, Integrating active learning and DFT for fast-tracking single-atom alloy catalysts in CO₂-to-fuel conversion, ACS Appl. Mater. Interfaces (2024).
[54] K. Tran, Z.W. Ulissi, Active learning across intermetallics to guide discovery of electrocatalysts for CO₂ reduction and H₂ evolution, Nat. Catal. 1 (2018) 696-703.
[55] X.H. Ge, J. Yin, Z.H. Ren, K.L. Yan, Y.D. Jing, Y.Q. Cao, N.N. Fei, X. Liu, X.N. Wang, X.G. Zhou, L.W. Chen, W.K. Yuan, X.Z. Duan, Atomic design of alkyne semihydrogenation catalysts via active learning, J. Am. Chem. Soc. 146 (7) (2024) 4993-5004.
[56] P.P. Zhang, J. Eun, M. Elkin, Y.Z. Zhao, R.L. Cantrell, T.R. Newhouse, A neural network model informs the total synthesis of clovane sesquiterpenoids, Nat. Synth. 2 (2023) 527-534.
[57] J.A. Bennett, N. Orouji, M. Khan, S. Sadeghi, J. Rodgers, M. Abolhasani, Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory, Nat. Chem. Eng. 1 (2024) 240-250.
[58] Y.Q. Pan, Q.F. Xiao, F.Y. Zhao, Z.H. Li, J.Y. Liu, S. Ullah, K.H. Lim, T.Y. Huang, Z.Y. Yu, C. Li, D.Y. Zhang, Q.Q. Xue, Q. Chen, S. Kawi, Y.J. Wang, G.S. Luo, Chat-microreactor: A large-language-model-based assistant for designing continuous flow systems, Chem. Eng. Sci. 311 (2025) 121567.
[59] Y.F. Shi, Z.X. Yang, S.C. Ma, P.L. Kang, C. Shang, P. Hu, Z.P. Liu, Machine learning for chemistry: Basics and applications, Engineering 27 (2023) 70-83.
[60] A.J. Myles, R.N. Feudale, Y. Liu, N.A. Woody, S.D. Brown, An introduction to decision tree modeling, J. Chemom. 18 (6) (2004) 275-285.
[61] Y.C. Lo, S.E. Rensi, W. Torng, R.B. Altman, Machine learning in chemoinformatics and drug discovery, Drug Discov. Today 23 (8) (2018) 1538-1546.
[62] A. Kadiyala, A. Kumar, Applications of Python to evaluate the performance of bagging methods, Environ. Prog. Sustain. Energy 37 (5) (2018) 1555-1559.
[63] J.B.O. Mitchell, Machine learning methods in chemoinformatics, Wiley Interdiscip. Rev. Comput. Mol. Sci. 4 (5) (2014) 468-481.
[64] B.Z. Dou, Z.L. Zhu, E. Merkurjev, L. Ke, L. Chen, J. Jiang, Y.Y. Zhu, J. Liu, B.G. Zhang, G.W. Wei, Machine learning methods for small data challenges in molecular science, Chem. Rev. 123 (13) (2023) 8736-8780.
[65] A. Jetybayeva, N. Borodinov, A.V. Ievlev, M.I.U. Haque, J. Hinkle, W.A. Lamberti, J.C. Meredith, D. Abmayr, O.S. Ovchinnikova, A review on recent machine learning applications for imaging mass spectrometry studies, J. Appl. Phys. 133 (2) (2023) 020702.
[66] B. Niu, Y.H. Jin, L. Lu, K.Y. Fen, L. Gu, Z.S. He, W.C. Lu, Y.X. Li, Y.D. Cai, Prediction of interaction between small molecule and enzyme using AdaBoost, Mol. Divers. 13 (3) (2009) 313-320.
[67] Z.W. Ge, S. Feng, C.C. Ma, K. Wei, K. Hu, W.J. Zhang, X.J. Dai, L.F. Fan, J.H. Hua, Quantifying and comparing the effects of key chemical descriptors on metal-organic frameworks water stability with CatBoost and SHAP, Microchem. J. 196 (2024) 109625.
[68] G.B. Goh, N.O. Hodas, A. Vishnu, Deep learning for computational chemistry, J. Comput. Chem. 38 (16) (2017) 1291-1307.
[69] Artificial neural networks: applications in chemical engineering.
[70] J. Bernal, K. Kushibar, D.S. Asfaw, S. Valverde, A. Oliver, R. Marti, X. Llado, Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: A review, Artif. Intell. Med. 95 (2019) 64-81.
[71] L.Y. Chen, S.B. Li, Q. Bai, J. Yang, S.L. Jiang, Y.M. Miao, Review of image classification algorithms based on convolutional neural networks, Remote. Sens. 13 (22) (2021) 4712.
[72] S. Wu, K. Roberts, S. Datta, J.C. Du, Z.C. Ji, Y.Q. Si, S. Soni, Q. Wang, Q. Wei, Y. Xiang, B. Zhao, H. Xu, Deep learning in clinical natural language processing: A methodical review, J. Am. Med. Inform. Assoc. 27 (3) (2020) 457-470.
[73] X.P. Qiu, T.X. Sun, Y.G. Xu, Y.F. Shao, N. Dai, X.J. Huang, Pre-trained models for natural language processing: A survey, Sci. China Technol. Sci. 63 (10) (2020) 1872-1897.
[74] L.Q. Huang, R.C. Luo, X. Liu, X. Hao, Spectral imaging with deep learning, Light Sci. Appl. 11 (1) (2022) 61.
[75] X.Y. Liu, H.L. An, W.S. Cai, X.G. Shao, Deep learning in spectral analysis: Modeling and imaging, Trac Trends Anal. Chem. 172 (2024) 117612.
[76] Q.Y. Xu, W.L. Du, J.J. Xu, J.K. Dong, Neural network-based source tracking of chemical leaks with obstacles, Chin. J. Chem. Eng. 33 (2021) 211-220.
[77] X.Y. Li, Y.Q. Xu, H.Q. Yao, K.J. Lin, Chemical space exploration based on recurrent neural networks: Applications in discovering kinase inhibitors, J. Cheminform. 12 (1) (2020) 42.
[78] K.D. Luong, A. Singh, Application of transformers in cheminformatics, J. Chem. Inf. Model. 64 (11) (2024) 4392-4409.
[79] J. Jiang, L. Ke, L. Chen, B.Z. Dou, Y.Y. Zhu, J. Liu, B.G. Zhang, T.S. Zhou, G.W. Wei, Transformer technology in molecular science, Wiley Interdiscip. Rev. Comput. Mol. Sci. 14 (4) (2024) e1725.
[80] Y.Q. Han, X.Y. Xu, C.Y. Hsieh, K.Y. Ding, H.X. Xu, R.J. Xu, T.J. Hou, Q. Zhang, H.J. Chen, Retrosynthesis prediction with an iterative string editing model, Nat. Commun. 15 (1) (2024) 6404.
[81] OpenAi, J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F.L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, R. Avila, I. Babuschkin, S. Balaji, V. Balcom, P. Baltescu, H. Bao, M. Bavarian, J. Belgum, I. Bello, J. Berdine, G. Bernadett-Shapiro, C. Berner, L. Bogdonoff, O. Boiko, M. Boyd, A.-L. Brakman, G. Brockman, T. Brooks, M. Brundage, K. Button, T. Cai, R. Campbell, A. Cann, B. Carey, C. Carlson, R. Carmichael, B. Chan, C. Chang, F. Chantzis, D. Chen, S. Chen, R. Chen, J. Chen, M. Chen, B. Chess, C. Cho, C. Chu, H.W. Chung, D. Cummings, J. Currier, Y. Dai, C. Decareaux, T. Degry, N. Deutsch, D. Deville, A. Dhar, D. Dohan, S. Dowling, S. Dunning, A. Ecoffet, A. Eleti, T. Eloundou, D. Farhi, L. Fedus, N. Felix, S.P. Fishman, J. Forte, I. Fulford, L. Gao, E. Georges, C. Gibson, V. Goel, T. Gogineni, G. Goh, R. Gontijo-Lopes, J. Gordon, M. Grafstein, S. Gray, R. Greene, J. Gross, S.S. Gu, Y. Guo, C. Hallacy, J. Han, J. Harris, Y. He, M. Heaton, J. Heidecke, C. Hesse, A. Hickey, W. Hickey, P. Hoeschele, B. Houghton, K. Hsu, S. Hu, X. Hu, J. Huizinga, S. Jain, S. Jain, J. Jang, A. Jiang, R. Jiang, H. Jin, D. Jin, S. Jomoto, B. Jonn, H. Jun, T. Kaftan, L. Kaiser, A. Kamali, I. Kanitscheider, N.S. Keskar, T. Khan, L. Kilpatrick, J.W. Kim, C. Kim, Y. Kim, J.H. Kirchner, J. Kiros, M. Knight, D. Kokotajlo, L. Kondraciuk, A. Kondrich, A. Konstantinidis, K. Kosic, G. Krueger, V. Kuo, M. Lampe, I. Lan, T. Lee, J. Leike, J. Leung, D. Levy, C.M. Li, R. Lim, M. Lin, S. Lin, M. Litwin, T. Lopez, R. Lowe, P. Lue, A. Makanju, K. Malfacini, S. Manning, T. Markov, Y. Markovski, B. Martin, K. Mayer, A. Mayne, B. McGrew, S.M. McKinney, C. McLeavey, P. McMillan, J. McNeil, D. Medina, A. Mehta, J. Menick, L. Metz, A. Mishchenko, P. Mishkin, V. Monaco, E. Morikawa, D. Mossing, T. Mu, M. Murati, O. Murk, D. Mely, A. Nair, R. Nakano, R. Nayak, A. Neelakantan, R. Ngo, H. Noh, L. Ouyang, C. O'Keefe, J. Pachocki, A. Paino, J. Palermo, A. Pantuliano, G. Parascandolo, J. Parish, E. Parparita, A. Passos, M. Pavlov, A. Peng, A. Perelman, F. de Avila Belbute Peres, M. Petrov, H.P. de Oliveira Pinto, Michael, Pokorny, M. Pokrass, V.H. Pong, T. Powell, A. Power, B. Power, E. Proehl, R. Puri, A. Radford, J. Rae, A. Ramesh, C. Raymond, F. Real, K. Rimbach, C. Ross, B. Rotsted, H. Roussez, N. Ryder, M. Saltarelli, T. Sanders, S. Santurkar, G. Sastry, H. Schmidt, D. Schnurr, J. Schulman, D. Selsam, K. Sheppard, T. Sherbakov, J. Shieh, S. Shoker, P. Shyam, S. Sidor, E. Sigler, M. Simens, J. Sitkin, K. Slama, I. Sohl, B. Sokolowsky, Y. Song, N. Staudacher, F.P. Such, N. Summers, I. Sutskever, J. Tang, N. Tezak, M.B. Thompson, P. Tillet, A. Tootoonchian, E. Tseng, P. Tuggle, N. Turley, J. Tworek, J.F.C. Uribe, A. Vallone, A. Vijayvergiya, C. Voss, C. Wainwright, J.J. Wang, A. Wang, B. Wang, J. Ward, J. Wei, C.J. Weinmann, A. Welihinda, P. Welinder, J. Weng, L. Weng, M. Wiethoff, D. Willner, C. Winter, S. Wolrich, H. Wong, L. Workman, S. Wu, J. Wu, M. Wu, K. Xiao, T. Xu, S. Yoo, K. Yu, Q. Yuan, W. Zaremba, R. Zellers, C. Zhang, M. Zhang, S. Zhao, T. Zheng, J. Zhuang, W. Zhuk, B. Zoph, GPT-4 Technical Report. 2024.

Intelligent chemical synthesis based on microchemical engineering technology

Intelligent chemical synthesis based on microchemical engineering technology

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Ali Tarik Karagoz, Omar Alqusair, Chao Liu, Jie Li. Advances in conceptual process design: From conventional strategies to AI-assisted methods[J]. 中国化学工程学报, 2025, 84(8): 60-76.
[2]	Dian Zhang, Bo Ouyang, Zheng-Hong Luo. Reaction process optimization based on interpretable machine learning and metaheuristic optimization algorithms[J]. 中国化学工程学报, 2025, 84(8): 77-85.
[3]	Zhi Ma, Peng Cui, Xu Wang, Lanyu Li, Haoxiang Xu, Adrian Fisher, Daojian Cheng. The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design[J]. 中国化学工程学报, 2025, 84(8): 117-132.
[4]	Jianfeng Jiao, Xi Gao, Jie Li. Pure component property estimation framework using explainable machine learning methods[J]. 中国化学工程学报, 2025, 84(8): 158-178.
[5]	Haijun Feng, Li Jiajia, Zhou Jian. Prediction of ionic liquid toxicity by interpretable machine learning[J]. 中国化学工程学报, 2025, 84(8): 201-210.
[6]	Feifei Chen, Zhenyuan Xiao, Zhongfan Luo, Peng Jiang, Jingjing Chen, Yuanhui Ji, Jiahua Zhu, Xiaohua Lu, Liwen Mu. Prediction of mass transfer performance in gas-liquid stirred bioreactor using machine learning[J]. 中国化学工程学报, 2025, 84(8): 211-226.
[7]	Yuan Tian, Honghua Zhang, Yueyang Qiao, Han Yang, Yanrong Liu, Xiaoyan Ji. Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning[J]. 中国化学工程学报, 2025, 84(8): 227-243.
[8]	Zhe Ding, Li Guo, Fang Bai, Chao Hua, Ping Lu, Jinyi Chen. Toward the rational design for low-temperature hydrogenation of silicon tetrachloride: Mechanism and data-driven interpretable descriptor[J]. 中国化学工程学报, 2025, 79(3): 172-184.
[9]	Hongwei Zhu, Junjie Feng, Ziyi Xu, Chunying Zhu, Youguang Ma, Wei Xu, Bing Sun, Taotao Fu. Bubble breakup in viscous liquids at a microfluidic T-junction[J]. 中国化学工程学报, 2025, 78(2): 44-57.
[10]	Mingqi Jiang, Zhuo Wang, Zhijian Sun, Jian Wang. A parallel chemical reaction optimization method based on preference-based multi-objective expected improvement[J]. 中国化学工程学报, 2025, 78(2): 82-92.
[11]	Wei Cheng, Huilin Wen, Xiaoqiang Chen, Shaobin Zhang, Ziyi Yu. Fluorosurfactants and their application in droplet microreactors: An overview[J]. 中国化学工程学报, 2025, 78(2): 314-326.
[12]	Yixin Wei, Leyu Shan, Tong Qiu, Diannan Lu, Zheng Liu. Machine learning-assisted retrosynthesis planning: Current status and future prospects[J]. 中国化学工程学报, 2025, 77(1): 273-292.
[13]	Jianzhi Wang, Xugen Li, Cheng Zhang, Yuan Pu, Jiawu Liu, Jie Liu, Yanping Liu, Xiao Lin, Faquan Yu. Polygonal mesopores microflower catalysts for the catalytic oxidation of 2-nitro-4-methylsulfonyltoluene to 2-nitro-4-methylsulfonylbenzoic acid in a continuous-flow microreactor[J]. 中国化学工程学报, 2024, 73(9): 212-221.
[14]	Yingxue Fu, Xinyan Liu, Jingzi Gao, Yang Lei, Yuqiu Chen, Xiangping Zhang. Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures[J]. 中国化学工程学报, 2024, 73(9): 244-255.
[15]	Pengcheng Lu, Yaoyao Li, Jianjun Zhang, Yuchao Zhao, Qingqiang Wang, Ying Chen, Nan Jin, Xiugang Yu. Continuous synthesis of N, N-dicyanoethylaniline in microreactors: Reaction kinetics and process intensification[J]. 中国化学工程学报, 2024, 72(8): 95-105.