Citation: Na Qin, Wenxin Guo, Fangxiu Li, Houfeng Zhang, Hong Liu, Chang Zhang, Lipiao Bao, Lei Liu, Muneerah Alomar, Siqi Zhao, Jian Zhang, Xing Lu. Recent advances in machine learning-driven discovery of alloy electrocatalysts for hydrogen evolution reaction[J]. Chinese Chemical Letters, ;2026, 37(3): 112021. doi: 10.1016/j.cclet.2025.112021 shu

Recent advances in machine learning-driven discovery of alloy electrocatalysts for hydrogen evolution reaction

Na Qin ^{a, 1} ,
Wenxin Guo ^{b, 1} ,
Fangxiu Li ^a ,
Houfeng Zhang ^a ,
Hong Liu ^a ,
Chang Zhang ^{a, c} ,
Lipiao Bao ^{a, c} ,
Lei Liu ^d ,
Muneerah Alomar ^e ,
Siqi Zhao ^{a, *,} ,
Jian Zhang ^{a, *,} ,
Xing Lu ^{a, c, *,}

a.
School of Chemistry and Chemical Engineering, Hainan University, Haikou 570228, China
b.
Key Laboratory of Precision and Intelligent/School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
c.
School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
d.
School of HUST-Wuxi Research Institute, Wuxi 214174, China
e.
Department of Physics, College of Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia

sqzhao@hainanu.edu.cn

zhangjian7@hust.edu.cn

lux@hust.edu.cn

Received Date: 24 June 2025
Revised Date: 20 October 2025
Accepted Date: 22 October 2025
Available Online: 24 October 2025

Figures(9)

The hydrogen evolution reaction (HER) is a pivotal process for clean energy conversion, yet the development of efficient and cost-effective electrocatalysts remains a major challenge. Alloy catalysts, with their tunable electronic properties and promising catalytic performance, have shown great potential for HER. However, the design of component types and ratios, along with structural optimization, has largely relied on traditional trial-and-error approaches, which are very complex and time-consuming. The rise of machine learning (ML) provides an efficient strategy for discovering and optimizing alloy catalysts by enabling rapid analysis of extensive experimental and simulation datasets. This review highlights the recent advances in applying ML techniques for the design and optimization of alloy electrocatalysts for HER, covering binary and multinary (ternary, quaternary and high-entropy alloys). In particular, by employing supervised learning and deep learning techniques, ML has achieved remarkable success in the rapid screening of alloy catalysts and in improving prediction accuracy. It also demonstrates the merit and capability of ML in accelerating this process. In the end, we discuss current challenges and future prospects for integrating ML into advanced HER catalysis, highlighting its potential to revolutionize catalyst development and promote sustainable hydrogen energy solutions.
- Hydrogen evolution reaction,
- Machine learning,
- Alloy catalysts,
- Artificial intelligence,
- Deep learning
1. [1]
  J. Li, Y. Sun, Z. Zhang, SmartMat 4 (2023) e1171. doi: 10.1002/smm2.1171
2. [2]
  T. Li, Y. Deng, X. Rong, et al., SmartMat 4 (2023) e1142. doi: 10.1002/smm2.1142
3. [3]
  S. Chu, A. Majumdar, Nature 488 (2012) 294–303. doi: 10.1038/nature11475
4. [4]
  B. Lu, D. Wang, C. Zhao, et al., Mater. Chem. Front. 6 (2022) 1681–1689. doi: 10.1039/d2qm00229a
5. [5]
  M.S. Dresselhaus, I.L. Thomas, Nature 414 (2001) 332–337. doi: 10.1038/35104599
6. [6]
  A. Kudo, Y. Miseki, Chem. Soc. Rev. 38 (2009) 253–278. doi: 10.1039/B800489G
7. [7]
  Y. Lei, Y. Wang, Y. Liu, et al., Angew. Chem. Int. Ed. 59 (2020) 20794–20812. doi: 10.1002/anie.201914647
8. [8]
  F. Sun, Q. Tang, D. Jiang, ACS Catal. 12 (2022) 8404–8433. doi: 10.1021/acscatal.2c02081
9. [9]
  S. Trasatti, J. Electroanal. Chem. 39 (1972) 163–184. doi: 10.1016/S0022-0728(72)80485-6
10. [10]
  P. Zhang, S. Hong, N. Song, et al., Chin. Chem. Lett. 35 (2024) 109073. doi: 10.1016/j.cclet.2023.109073
11. [11]
  T. Yang, SmartMat 5 (2024) e1204. doi: 10.1002/smm2.1204
12. [12]
  J. Liu, Z. Wang, L. Kou, Y. Gu, ACS Sustain. Sustain, Chem. Eng. 11 (2023) 467–481. doi: 10.1021/acssuschemeng.2c05212
13. [13]
  C. Zhou, R. Yuan, W. Liao, et al., Rare Met. 43 (2024) 2884–2890. doi: 10.1007/s12598-023-02559-8
14. [14]
  W. Chen, Y. Jiang, F. Tan, et al., Rare Met. 44 (2025) 5824–5843. doi: 10.1007/s12598-025-03331-w
15. [15]
  Q. Tao, K. Bao, J. Cao, et al., Rare Met. 44 (2025) 7859–7875. doi: 10.1007/s12598-025-03434-4
16. [16]
  S. Yoshitake, V. Narayan, H. Harada, et al., ISIJ Int. 38 (1998) 495–502. doi: 10.2355/isijinternational.38.495
17. [17]
  J. Cai, X. Chu, K. Xu, H. Li, J. Wei, Nanoscale Adv. 2 (2020) 3115–3130. doi: 10.1039/d0na00388c
18. [18]
  S. Wei, S. Baek, H. Yue, et al., J. Electrochem. Soc. 168 (2021) 126523. doi: 10.1149/1945-7111/ac41f1
19. [19]
  M. Meuwly, Chem. Rev. 121 (2021) 10218–10239. doi: 10.1021/acs.chemrev.1c00033
20. [20]
  Y. Shi, Z. Yang, S. Ma, et al., Engineering 27 (2023) 70–83. doi: 10.1016/j.eng.2023.04.013
21. [21]
  H. Huo, Z. Rong, O. Kononova, et al., npj Comput. Mater. 5 (2019) 62. doi: 10.1038/s41524-019-0204-1
22. [22]
  H. Liu, A. Yang, C. Zhong, et al., ACS Energy Lett. 10 (2025) 1884–1891. doi: 10.1021/acsenergylett.5c00626
23. [23]
  L. Chen, Y. Tian, X. Hu, et al., Adv. Funct. Mater. 32 (2022) 2208418. doi: 10.1002/adfm.202208418
24. [24]
  X. Li, R. Chiong, Z. Hu, A.J. Page, J. Phys. Chem. Lett. 12 (2021) 7305–7311. doi: 10.1021/acs.jpclett.1c01851
25. [25]
  M. Kim, M.Y. Ha, W. Jung, et al., Adv. Mater. 34 (2022) 2108900. doi: 10.1002/adma.202108900
26. [26]
  L. Huang, Z. Gariepy, E. Halpren, et al., Small Methods 5 (2024) 2401224.
27. [27]
  P. Domingos, Commun. ACM 55 (2012) 78–87. doi: 10.1145/2347736.2347755
28. [28]
  C. Xiouras, F. Cameli, G.L. Quilló, et al., Chem. Rev. 122 (2022) 13006–13042. doi: 10.1021/acs.chemrev.2c00141
29. [29]
  A. Jain, S.P. Ong, G. Hautier, et al., APL Mater. 1 (2013) 011002. doi: 10.1063/1.4812323
30. [30]
  N. Artrith, K.T. Butler, F.X. Coudert, et al., Nat. Chem. 13 (2021) 505–508. doi: 10.1038/s41557-021-00716-z
31. [31]
  K.T. Winther, M.J. Hoffmann, J.R. Boes, et al., Sci. Data 6 (2019) 75. doi: 10.1038/s41597-019-0081-y
32. [32]
  J. Deng, J. Pan, Y. Zhang, S. Du, Nano Lett. 23 (2023) 4634–4641. doi: 10.1021/acs.nanolett.3c01237
33. [33]
  Y. Iwasaki, R. Sawada, E. Saitoh, M. Ishida, Commun. Mater. 2 (2021) 31. doi: 10.1038/s43246-021-00135-0
34. [34]
  F.A. Rasmussen, K.S. Thygesen, J. Phys. Chem. C 119 (2015) 13169–13183. doi: 10.1021/acs.jpcc.5b02950
35. [35]
  A. Tropsha, K.C. Mills, A.J. Hickey, Nat. Nanotechnol. 12 (2017) 1111–1114. doi: 10.1038/nnano.2017.233
36. [36]
  M. Quirós, S. Gražulis, S. Girdzijauskaitė, A. Merkys, A. Vaitkus, J. Cheminform. 10 (2018) 23. doi: 10.1186/s13321-018-0279-6
37. [37]
  A. Belsky, M. Hellenbrandt, V.L. Karen, P. Luksch, Acta Cryst. B: Struct. Sci. 58 (2002) 364–369. doi: 10.1107/S0108768102006948
38. [38]
  R. Guha, E. Willighagen, J. Cheminform. 12 (2020) 4. doi: 10.1186/s13321-019-0406-z
39. [39]
  S. Curtarolo, W. Setyawan, S. Wang, et al., Comput. Mater. Sci. 58 (2012) 227–235. doi: 10.1016/j.commatsci.2012.02.002
40. [40]
  S. Kirklin, J.E. Saal, B. Meredig, et al., npj Comput. Mater. 1 (2015) 15010. doi: 10.1038/npjcompumats.2015.10
41. [41]
  P. Villars, M. Berndt, K. Brandenburg, et al., J. Alloy. Compd. 367 (2004) 293–297. doi: 10.1016/j.jallcom.2003.08.058
42. [42]
  C.R. Groom, F.H. Allen, Angew. Chem. Int. Ed. 53 (2014) 662–671. doi: 10.1002/anie.201306438
43. [43]
  Y. Xu, M. Yamazaki, P. Villars, Jpn. J. Appl. Phys. 50 (2011) 11RH02. doi: 10.1143/JJAP.50.11RG02
44. [44]
  A. Zakutayev, N. Wunder, M. Schwarting, et al., Sci. Data 5 (2018) 180053. doi: 10.1038/sdata.2018.53
45. [45]
  A.M. Khan, F.A. Loan, U.Y. Parray, S. Rashid, Inf. Discov. Deliv. 52 (2024) 53–61.
46. [46]
  M.N. Gjerding, A. Taghizadeh, A. Rasmussen, et al., 2D Mater. 8 (2021) 044002. doi: 10.1088/2053-1583/ac1059
47. [47]
  S. Haastrup, M. Strange, M. Pandey, et al., 2D Mater. 5 (2018) 042002. doi: 10.1088/2053-1583/aacfc1
48. [48]
  J. Zhou, L. Shen, M.D. Costa, et al., Sci. Data 6 (2019) 86. doi: 10.1038/s41597-019-0097-3
49. [49]
  L. Chanussot, A. Das, S. Goyal, et al., ACS Catal. 11 (2021) 6059–6072. doi: 10.1021/acscatal.0c04525
50. [50]
  R. Tran, J. Lan, M. Shuaibi, et al., ACS Catal. 13 (2023) 3066–3084. doi: 10.1021/acscatal.2c05426
51. [51]
  M. Liu, S. Meng, Sci. Sin. Chim. 53 (2023) 19–25. doi: 10.1360/ssc-2022-0167
52. [52]
  A.C. Vaucher, F. Zipoli, J. Geluykens, et al., Nat. Commun. 11 (2020) 3601. doi: 10.1038/s41467-020-17266-6
53. [53]
  T. He, W. Sun, H. Huo, et al., Chem. Mater. 32 (2020) 7861–7873. doi: 10.1021/acs.chemmater.0c02553
54. [54]
  P. Shetty, A.C. Rajan, C. Kuenneth, et al., npj Comput. Mater. 9 (2023) 52. doi: 10.1038/s41524-023-01003-w
55. [55]
  P. Shetty, R. Ramprasad, iScience 24 (2021) 101922. doi: 10.1016/j.isci.2020.101922
56. [56]
  P. Shetty, R. Ramprasad, J. Chem. Inf. Model. 61 (2021) 5377–5385. doi: 10.1021/acs.jcim.1c00554
57. [57]
  T. Gupta, M. Zaki, N.M.A. Krishnan, Mausam npj Comput. Mater. 8 (2022) 102. doi: 10.1038/s41524-022-00784-w
58. [58]
  Z. Wang, O. Kononova, K. Cruse, et al., Sci. Data 9 (2022) 231. doi: 10.1038/s41597-022-01317-2
59. [59]
  N. Qin, L. Liu, L. Wei, H. Chen, J. Alloys Compd. Commun. 6 (2025) 100067.
60. [60]
  J. Li, N. Wu, J. Zhang, et al., Nano-Micro Lett. 15 (2023) 227. doi: 10.1007/s40820-023-01192-5
61. [61]
  Y. Ding, L. Fan, X. Liu, Energy Build. 249 (2021) 111208. doi: 10.1016/j.enbuild.2021.111208
62. [62]
  M. Karthikeyan, D.M. Mahapatra, A.S.A. Razak, et al., Catal. Rev. Sci. Eng. 66 (2024) 997–1027. doi: 10.1080/01614940.2022.2103980
63. [63]
  B. Wang, F. Zhang, Angew. Chem. Int. Ed. 61 (2022) e202111026. doi: 10.1002/anie.202111026
64. [64]
  C. Wang, X. Wang, T. Zhang, et al., J. Mater. Chem. A 10 (2022) 18195–18205. doi: 10.1039/d2ta02837a
65. [65]
  P. Riley, Nature 572 (2019) 27–29. doi: 10.1038/d41586-019-02307-y
66. [66]
  Y. Xu, X. Zhang, Y. Liu, et al., Environ. Sci. Pollut. Res. 30 (2022) 11302–11320. doi: 10.1007/s11356-022-24728-5
67. [67]
  A.R. Oganov, C.J. Pickard, Q. Zhu, R.J. Needs, Nat. Rev. Mater. 4 (2019) 331–348. doi: 10.1038/s41578-019-0101-8
68. [68]
  E.A. Olivetti, J.M. Cole, E. Kim, et al., Appl. Phys. Rev. 7 (2020) 041317. doi: 10.1063/5.0021106
69. [69]
  G.L.W. Hart, T. Mueller, C. Toher, S. Curtarolo, Nat. Rev. Mater. 6 (2021) 730–755. doi: 10.1038/s41578-021-00340-w
70. [70]
  M. Hu, Q. Tan, R. Knibbe, et al., Mater. Sci. Eng. R.: Rep. 155 (2023) 100746. doi: 10.1016/j.mser.2023.100746
71. [71]
  J. Liu, C. Lee, Y. Hu, et al., SmartMat 4 (2023) e1210. doi: 10.1002/smm2.1210
72. [72]
  Y. Jeong, T. Begildayeva, J. Theerthagiri, et al., SmartMat 5 (2024) e1281. doi: 10.1002/smm2.1281
73. [73]
  M.O.J. Jäger, E.V. Morooka, F. Federici Canova, L. Himanen, A.S. Foster, npj Comput. Mater. 4 (2018) 37. doi: 10.1038/s41524-018-0096-5
74. [74]
  M.O.J. Jäger, Y.S. Ranawat, F.F. Canova, E.V. Morooka, A.S. Foster, ACS Comb. Sci. 22 (2020) 768–781. doi: 10.1021/acscombsci.0c00102
75. [75]
  X. Mao, L. Wang, Y. Xu, et al., npj Comput. Mater. 7 (2021) 46. doi: 10.1038/s41524-021-00514-8
76. [76]
  J. Kang, J. Kloppenburg, J. Sheng, et al., ACS Catal. 15 (2025) 9928–9939. doi: 10.1021/acscatal.4c07859
77. [77]
  J. Zhang, Y. Wang, X. Zhou, et al., Nanoscale 15 (2023) 11072–11082. doi: 10.1039/d3nr01442h
78. [78]
  K. Tran, Z.W. Ulissi, Nat. Catal. 1 (2018) 696–703. doi: 10.1038/s41929-018-0142-1
79. [79]
  H. Li, S. Xu, M. Wang, et al., J. Mater. Chem. A 8 (2020) 17987–17997. doi: 10.1039/d0ta04615a
80. [80]
  T. Wang, J. Li, W. Shu, et al., Chin. J. Chem. Phys. 33 (2020) 703–711. doi: 10.1063/1674-0068/cjcp2004049
81. [81]
  C. MartínezAlonso, V. VassilevGalindo, B.M. Comer, et al., Catal. Sci. Technol. 14 (2024) 3784–3799. doi: 10.1039/d4cy00491d
82. [82]
  R. De PazCastany, K. Eiler, A. Nicolenco, et al., ChemSusChem 18 (2025) e202400444. doi: 10.1002/cssc.202400444
83. [83]
  X. Sun, Y. Song, G. Jiang, X. Lan, C. Xu, Sci. China Mater. 67 (2024) 1–17.
84. [84]
  B. Qiao, A. Wang, X. Yang, et al., Nat. Chem. 3 (2011) 634–641. doi: 10.1038/nchem.1095
85. [85]
  R.T. Hannagan, G. Giannakakis, M. Flytzani Stephanopoulos, E.C.H. Sykes, Chem. Rev. 120 (2020) 12044–12088. doi: 10.1021/acs.chemrev.0c00078
86. [86]
  Y. Da, R. Jiang, Z. Tian, et al., SmartMat 4 (2023) e1136. doi: 10.1002/smm2.1136
87. [87]
  M.A. Deshmukh, A. Bakandritsos, R. Zbořil, Nano-Micro Lett. 17 (2025) 1.
88. [88]
  B. Geng, X. Chu, L. Liu, et al., Chin. Chem. Lett. 35 (2024) 108924. doi: 10.1016/j.cclet.2023.108924
89. [89]
  G. Kyriakou, M.B. Boucher, A.D. Jewell, et al., Science 335 (2012) 1209–1212. doi: 10.1126/science.1215864
90. [90]
  J. Liu, J. Zhang, H. Deng, et al., Chin. Chem. Lett. 36 (2025) 110656. doi: 10.1016/j.cclet.2024.110656
91. [91]
  K.K. Rao, Q.K. Do, K. Pham, D. Maiti, L.C. Grabow, Top. Catal. 63 (2020) 728–741. doi: 10.1007/s11244-020-01267-2
92. [92]
  Z. Han, D. Sarker, R. Ouyang, et al., Nat. Commun. 12 (2021) 1833. doi: 10.1038/s41467-021-22048-9
93. [93]
  C. Zhou, J.Y. Zhao, P.F. Liu, et al., Chem. Sci. 12 (2021) 10634–10642. doi: 10.1039/d1sc01018b
94. [94]
  G.O. Kayode, A.F. Hill, M.M. Montemore, J. Mater. Chem. A 11 (2023) 19128–19137. doi: 10.1039/d3ta02830e
95. [95]
  Y. Wang, F. Ma, G. Zhang, et al., Nano Res. 17 (2024) 9397–9427. doi: 10.1007/s12274-024-6940-7
96. [96]
  X. Liang, N. Fu, S. Yao, Z. Li, Y. Li, J. Am. Chem. Soc. 144 (2022) 18155–18174. doi: 10.1021/jacs.1c12642
97. [97]
  L. Wu, T. Li, Mater. Chem. Front. 7 (2023) 4445–4459. doi: 10.1039/d3qm00661a
98. [98]
  L. Zhang, X. Guo, S. Zhang, S. Huang, J. Mater. Chem. A 10 (2022) 11600–11612. doi: 10.1039/d2ta02050e
99. [99]
  L. Zhang, X. Guo, S. Zhang, T. Frauenheim, S. Huang, Adv. Energy Mater. 14 (2024) 2302754. doi: 10.1002/aenm.202302754
100. [100]
  K. Boonpalit, Y. Wongnongwa, C. Prommin, S. Nutanong, S. Namuangruk, ACS Appl. Mater. Interfaces 15 (2023) 12936–12945. doi: 10.1021/acsami.2c19391
101. [101]
  C. Wei, D. Shi, Z. Yang, et al., J. Mater. Chem. A 11 (2023) 18168–18178. doi: 10.1039/d3ta02332j
102. [102]
  J. Wang, W. Xu, J. He, et al., Appl. Surf. Sci. 659 (2024) 159942. doi: 10.1016/j.apsusc.2024.159942
103. [103]
  C. Chowdhury, E. Karthikraja, V. Subramanian, Phys. Chem. Chem. Phys. 26 (2024) 25143–25155. doi: 10.1039/d4cp03171g
104. [104]
  W. Liang, A. Aierken, X. Rong, et al., Appl. Surf. Sci. 682 (2025) 161745. doi: 10.1016/j.apsusc.2024.161745
105. [105]
  H. Zhang, Q. Wei, S. Wei, et al., Mol. Catal. 570 (2025) 114649.
106. [106]
  J. Schmidt, L. Chen, S. Botti, M.A.L. Marques, J. Chem. Phys. 148 (2018) 241728. doi: 10.1063/1.5020223
107. [107]
  J. Yoon, Z. Cao, R.K. Raju, et al., Mach. Learn.: Sci. Technol. 2 (2021) 045018. doi: 10.1088/2632-2153/ac191c
108. [108]
  G. Henkelman, B.P. Uberuaga, H. Jónsson, J. Chem. Phys. 113 (2000) 9901–9904. doi: 10.1063/1.1329672
109. [109]
  S. Gao, H. Zhen, B. Wen, J. Ma, X. Zhang, Nanoscale 14 (2022) 2660–2667. doi: 10.1039/d1nr07661b
110. [110]
  M. Kim, Y. Kim, M.Y. Ha, et al., Adv. Mater. 35 (2023) 2211497. doi: 10.1002/adma.202211497
111. [111]
  N.K. Pandit, D. Roy, S.C. Mandal, B. Pathak, J. Phys. Chem. Lett. 13 (2022) 7583–7593. doi: 10.1021/acs.jpclett.2c01401
112. [112]
  C. Liu, S. Zhu, F. Sun, et al., Chem. Eng. J. 476 (2023) 146918. doi: 10.1016/j.cej.2023.146918
113. [113]
  X. Zhang, K. Li, B. Wen, J. Ma, D. Diao, Chin. Chem. Lett. 34 (2023) 107833. doi: 10.1016/j.cclet.2022.107833
114. [114]
  B. Cantor, I.T.H. Chang, P. Knight, A.J.B. Vincent, Mater. Sci. Eng. R.: Rep. 375–377 (2004) 213–218.
115. [115]
  J.W. Yeh, S.K. Chen, S.J. Lin, et al., Adv. Eng. Mater. 6 (2004) 299–303. doi: 10.1002/adem.200300567
116. [116]
  Y. Zhou, X. Shen, M. Wang, et al., Sci. China Mater. 66 (2023) 2527–2544. doi: 10.1007/s40843-022-2436-6
117. [117]
  M. Tsai, J. Yeh, Mater. Res. Lett. 2 (2014) 107–123. doi: 10.1080/21663831.2014.912690
118. [118]
  J. Kwon, S. Sun, S. Choi, et al., Adv. Mater. 35 (2023) 2300091. doi: 10.1002/adma.202300091
119. [119]
  D. Wu, K. Kusada, T. Yamamoto, et al., Chem. Sci. 11 (2020) 12731–12736. doi: 10.1039/d0sc02351e
120. [120]
  X. Shan, Y. Pan, F. Cai, et al., Nano Lett. 24 (2024) 11632–11640. doi: 10.1021/acs.nanolett.4c03208
121. [121]
  L. Li, Z. Zhang, SmartMat 5 (2024) e1239. doi: 10.1002/smm2.1239
122. [122]
  D. Wang, Z. Chen, Y. Wu, et al., SmartMat 4 (2023) e1117. doi: 10.1002/smm2.1117
123. [123]
  L. Sun, K. Wen, G. Li, et al., ACS Mater. Au 4 (2024) 547–556. doi: 10.1021/acsmaterialsau.4c00080
124. [124]
  M. Sun, Y. Li, S. Wang, et al., Nanoscale 15 (2023) 13515–13531. doi: 10.1039/d3nr01836a
125. [125]
  X. Wan, Z. Li, W. Yu, et al., Adv. Mater. 37 (2023) e2305192.
126. [126]
  Y.V. Krishna, U.K. Jaiswal, R.M. R, Scr. Mater. 197 (2021) 113804. doi: 10.1016/j.scriptamat.2021.113804
127. [127]
  N. Islam, W. Huang, H.L. Zhuang, Comput. Mater. Sci. 150 (2018) 230–235. doi: 10.1016/j.commatsci.2018.04.003
128. [128]
  Y. Maruta, K. Kusada, D. Wu, et al., Chem. Commun. 58 (2022) 6421–6424. doi: 10.1039/d2cc01866g
129. [129]
  W.A. Saidi, T. Nandi, T. Yang, Electrochem. Sci. Adv. 3 (2023) e2100224. doi: 10.1002/elsa.202100224
130. [130]
  Z. Yang, Q. Yin, Z. Yin, et al., Prog. Nat. Sci. Mater. Int. 3 (2025) 631–637. doi: 10.1016/j.pnsc.2025.05.001
1. [1]
  Ke Ma , Shiqiang Feng , Haihui Hu , Yimeng Cai , Dechao Chen , Lili Han . Deep learning-based workflow for atomic image denoising and chemical identification. Chinese Journal of Structural Chemistry, 2025, 44(5): 100572-100572. doi: 10.1016/j.cjsc.2025.100572
2. [2]
  Xi Xue , Kai Chen , Hanyu Sun , Xiangying Liu , Xue Liu , Shize Li , Jingjie Yan , Yu Peng , Mohammad S. Mubarak , Ahmed Al-Harrasi , Hai-Yu Hu , Yafeng Deng , Xiandao Pan , Xiaojian Wang . Bidirectional Chemical Intelligent Net: A unified deep learning–based framework for predicting chemical reaction. Chinese Chemical Letters, 2025, 36(11): 110968-. doi: 10.1016/j.cclet.2025.110968
3. [3]
  Jieyu Liu , Junze Zhang , Haigang Deng , Shuoao Wang , Xingxing Jiang , Li Wang , Changhong Wang . Understanding the activity origin of Pd-anchored single-atom alloy catalysts for NO-to-NH₃ conversion by DFT studies and machine learning. Chinese Chemical Letters, 2025, 36(12): 110656-. doi: 10.1016/j.cclet.2024.110656
4. [4]
  Xu He , Wenjie Gao , Jinglei Xu , Zhanjun Cheng , Wenchao Peng , Beibei Yan , Guanyi Chen , Ning Li . Machine learning-assisted construction of C=O and pyridinic N active sites in sludge-based catalysts. Chinese Chemical Letters, 2025, 36(12): 111019-. doi: 10.1016/j.cclet.2025.111019
5. [5]
  Hao Chen , Haiyuan Liao , Qi Zhou , Yang Liu , Guojun Liu , Yuan Yao . Electronegativity-oriented coordination regulation of main-group metal single-atom catalysts for oxygen reduction to H₂O₂: A combined study of first-principles and machine learning. Chinese Chemical Letters, 2026, 37(3): 110711-. doi: 10.1016/j.cclet.2024.110711
6. [6]
  Zonglin Li , Shihua Zou , Zining Wang , Georgeta Postole , Liang Hu , Hongying Zhao . Machine learning in electrochemical oxidation process: A mini-review. Chinese Chemical Letters, 2025, 36(8): 110526-. doi: 10.1016/j.cclet.2024.110526
7. [7]
  Yunzhe Zheng , Si Sun , Jiali Liu , Qingyu Zhao , Heng Zhang , Jing Zhang , Peng Zhou , Zhaokun Xiong , Chuan-Shu He , Bo Lai . Application of machine learning for material prediction and design in the environmental remediation. Chinese Chemical Letters, 2025, 36(9): 110722-. doi: 10.1016/j.cclet.2024.110722
8. [8]
  Honglin Chen , Rupeng Wang , Zixiang He , Shih-Hsin Ho . Data-driven insights into nonradical activation mechanisms for biochar inverse design: A synergistic approach using DFT and machine learning with meta-analysis. Chinese Chemical Letters, 2026, 37(2): 111372-. doi: 10.1016/j.cclet.2025.111372
9. [9]
  Zixing Xu , Ruiying Chen , Chuanming Hao , Qionghong Xie , Chunhui Deng , Nianrong Sun . Peptidome data-driven comprehensive individualized monitoring of membranous nephropathy with machine learning. Chinese Chemical Letters, 2024, 35(5): 108975-. doi: 10.1016/j.cclet.2023.108975
10. [10]
  Lu Li , Jianing Shen , Qinkun Xiao , Chaozheng He , Jinzhou Zheng , Chaoqin Chu , Chen Chen . Stable crystal structure prediction using machine learning-based formation energy and empirical potential function. Chinese Chemical Letters, 2025, 36(11): 110421-. doi: 10.1016/j.cclet.2024.110421
11. [11]
  Han-Bin Liu , Xiaoyu Cheng , Zhou Guo , Juan Yang , Fuwen Tan , Donghui Lan , Jian-Ping Tan , Bing Yi , Weixin Zhai , Qing-Hui Guo . CrownBind-IA: A machine learning model predicting binding constants between crown ethers and alkali metal ions. Chinese Chemical Letters, 2025, 36(12): 111149-. doi: 10.1016/j.cclet.2025.111149
12. [12]
  Shuang Li , Penghui Yuan , Xinyi Zhang , Meiru Liu , Dezhi Yang , Linglei Kong , Li Zhang , Yang Lu , Guanhua Du . Revolutionizing sepsis therapy: Machine learning-driven co-crystallization reveals emodin's therapeutic potential. Chinese Chemical Letters, 2026, 37(2): 111289-. doi: 10.1016/j.cclet.2025.111289
13. [13]
  Mengzhao Liu , Jie Yin , Chengjian Wang , Weiji Wang , Yuan Gao , Mengxia Yan , Ping Geng . P doped Ni₃S₂ and Ni heterojunction bifunctional catalysts for electrocatalytic 5-hydroxymethylfurfural oxidation coupled hydrogen evolution reaction. Chinese Chemical Letters, 2025, 36(9): 111271-. doi: 10.1016/j.cclet.2025.111271
14. [14]
  Xiangyuan Zhao , Jinjin Wang , Jinzhao Kang , Xiaomei Wang , Hong Yu , Cheng-Feng Du . Ni nanoparticles anchoring on vacuum treated Mo2TiC2Tx MXene for enhanced hydrogen evolution activity. Chinese Journal of Structural Chemistry, 2023, 42(10): 100159-100159. doi: 10.1016/j.cjsc.2023.100159
15. [15]
  Haibin Yang , Duowen Ma , Yang Li , Qinghe Zhao , Feng Pan , Shisheng Zheng , Zirui Lou . Mo doped Ru-based cluster to promote alkaline hydrogen evolution with ultra-low Ru loading. Chinese Journal of Structural Chemistry, 2023, 42(11): 100031-100031. doi: 10.1016/j.cjsc.2023.100031
16. [16]
  Qingyun Hu , Wei Wang , Junyuan Lu , He Zhu , Qi Liu , Yang Ren , Hong Wang , Jian Hui . High-throughput screening of high energy density LiMn_1-xFe_xPO₄ via active learning. Chinese Chemical Letters, 2025, 36(2): 110344-. doi: 10.1016/j.cclet.2024.110344
17. [17]
  Yunxin Li , Jinghui Zhang , Jisen Chen , Feng Zhu , Zhiqiang Liu , Peng Bao , Wei Shen , Sheng Tang . Detection of SARS-CoV-2 based on artificial intelligence-assisted smartphone: A review. Chinese Chemical Letters, 2024, 35(7): 109220-. doi: 10.1016/j.cclet.2023.109220
18. [18]
  Yuting Wu , Haifeng Lv , Xiaojun Wu . Design of two-dimensional porous covalent organic framework semiconductors for visible-light-driven overall water splitting: A theoretical perspective. Chinese Journal of Structural Chemistry, 2024, 43(11): 100375-100375. doi: 10.1016/j.cjsc.2024.100375
19. [19]
  Rui Deng , Wenjie Jiang , Tianqi Yu , Jiali Lu , Boyao Feng , Panagiotis Tsiakaras , Shibin Yin . Cycad-leaf-like crystalline-amorphous heterostructures for efficient urea oxidation-assisted water splitting. Chinese Journal of Structural Chemistry, 2024, 43(7): 100290-100290. doi: 10.1016/j.cjsc.2024.100290
20. [20]
  Wenjie Jiang , Zhixiang Zhai , Xiaoyan Zhuo , Jia Wu , Boyao Feng , Tianqi Yu , Huan Wen , Shibin Yin . Revealing the reactant adsorption role of high-valence WO3 for boosting urea-assisted water splitting. Chinese Journal of Structural Chemistry, 2025, 44(3): 100519-100519. doi: 10.1016/j.cjsc.2025.100519

Get Citation

PDF

XML

Metrics

PDF Downloads(0)
Abstract views(25)
HTML views(3)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142