Citation: Zicheng Wan, Sisi Lao, Weifeng Zhang, Qiuju Zhang, Ziqi Tian. Convolutional neural network enabled prediction of surface features in CuAgAu alloys[J]. Chinese Chemical Letters, ;2026, 37(12): 111911. doi: 10.1016/j.cclet.2025.111911 shu

Convolutional neural network enabled prediction of surface features in CuAgAu alloys

Zicheng Wan ^{a, c} ,
Sisi Lao ^{a, c} ,
Weifeng Zhang ^{b, d, *,} ,
Qiuju Zhang ^a ,
Ziqi Tian ^{a, *,}

a.
Zhejiang Key Laboratory of Advanced Fuel Cells and Electrolyzers Technology, Ningbo Institute of Material Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
b.
Zhejiang Pharmaceutical University, Ningbo 315500, China
c.
School of Materials Science and Chemical Engineering, Ningbo University, Ningbo 313001, China
d.
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 313001, China

zhangweifeng0506@163.com

tianziqi@nimte.ac.cn

Received Date: 21 July 2025
Revised Date: 23 September 2025
Accepted Date: 27 September 2025
Available Online: 30 September 2025

Figures(4)

The rational design of high-performance alloy materials requires a deep understanding of electronic structures, which governs key material properties. The electronic density of states (DOS) is an expression of the electronic structure, normally derived from conventional first principle calculation. However, simulation of multi-component alloy requires consideration of a huge phase space, which is computationally prohibitive. Here, we present a machine learning (ML) combining a modified crystal graph convolutional neural network (CGCNN) and a convolutional neural network (CNN) to predict DOS and related surface features in CuAgAu alloy system. The CGCNN model, trained on 800 calculated structures, predicts site-resolved DOS with mean absolute error below 0.1 and median R² values approaching 0.98. By integrating predicted DOS with a CNN model, CO adsorption energies on various surface sites were predicted, achieving MAE < 0.05 eV. The integrated model generalizes well to ternary and binary alloy compositions, demonstrating utility in high-throughput screening. This work bridges ML with physical insights, enabling rapid exploration of alloy surfaces for catalytic applications while circumventing computational costs of ab initio methods.
- Machine learning,
- Density of states,
- Convolutional neural network,
- Adsorption energy,
- CuAgAu alloy
1. [1]
  E.A. Starke Jr., J.T. Staley, Prog. Aerosp. Sci. 32 (1996) 131–172. doi: 10.1016/0376-0421(95)00004-6
2. [2]
  H. Ohno, P. Nuss, W.Q. Chen, T.E. Graedel, Environ. Sci. Technol. 50 (2016) 4082–4090. doi: 10.1021/acs.est.5b05093
3. [3]
  M. Yang, J. Ding, Z. Wang, et al., Chin. Chem. Lett. 36 (2025) 110861. doi: 10.1016/j.cclet.2025.110861
4. [4]
  Y. Liu, H. Lu, T. Yang, et al., Chin. Chem. Lett. 35 (2024) 109492. doi: 10.1016/j.cclet.2024.109492
5. [5]
  M. Luneau, J.S. Lim, D.A. Patel, et al., Chem. Rev. 120 (2020) 12834–12872. doi: 10.1021/acs.chemrev.0c00582
6. [6]
  S. Zafeiratos, S. Piccinin, D. Teschner, Catal. Sci. Technol. 2 (2012) 1787–1801. doi: 10.1039/c2cy00487a
7. [7]
  J.-F. Deng, H. Li, W. Wang, Catal. Today 51 (1999) 113–125. doi: 10.1016/S0920-5861(99)00013-9
8. [8]
  L. Yu, K. Zeng, C. Li, et al., Carbon Energy 4 (2022) 731–761. doi: 10.1002/cey2.228
9. [9]
  T.Y. Jeon, S.H. Yu, S.J. Yoo, et al., Carbon Energy 3 (2021) 375–383. doi: 10.1002/cey2.82
10. [10]
  C.D. Taylor, P. Lu, J. Saal, et al., npj Mater. Degrad. 2 (2018) 6. doi: 10.1038/s41529-018-0027-4
11. [11]
  C.H. Bartholomew, Appl. Catal. A: Gen. 212 (2001) 17–60. doi: 10.1016/S0926-860X(00)00843-7
12. [12]
  L. Chen, Z. Chen, X. Yao, et al., J. Mater. Inform. 2 (2022) 23.
13. [13]
  M.Y. Toriyama, A.M. Ganose, M. Dylla, et al., Mater. Today Electron. 1 (2022) 100002. doi: 10.1016/j.mtelec.2022.100002
14. [14]
  A. Anthore, H. Pothier, D. Esteve, Phys. Rev. Lett. 90 (2003) 127001. doi: 10.1103/PhysRevLett.90.127001
15. [15]
  P. LeClair, J. Kohlhepp, C. van De Vin, et al., Phys. Rev. Lett. 88 (2002) 107201. doi: 10.1103/PhysRevLett.88.107201
16. [16]
  S. Broderick, K. Rajan, Europhys. Lett. 95 (2011) 57005. doi: 10.1209/0295-5075/95/57005
17. [17]
  B.C. Yeo, D. Kim, C. Kim, S.S. Han, Sci. Rep. 9 (2019) 5879. doi: 10.1038/s41598-019-42277-9
18. [18]
  T. Xie, J.C. Grossman, Phys. Rev. Lett. 120 (2018) 145301. doi: 10.1103/PhysRevLett.120.145301
19. [19]
  C.W. Park, C. Wolverton, Phys. Rev. Mater. 4 (2020) 063801. doi: 10.1103/PhysRevMaterials.4.063801
20. [20]
  S.Y. Louis, Y. Zhao, A. Nasiri, et al., Phys. Chem. Chem. Phys. 22 (2020) 18141–18148. doi: 10.1039/d0cp01474e
21. [21]
  L. Li, J. Shen, Q. Xiao, et al., Chin. Chem. Lett. 36 (2025) 110421. doi: 10.1016/j.cclet.2024.110421
22. [22]
  A.H. Larsen, J.J. Mortensen, J. Blomqvist, et al., J. Phys. Condens. Matter 29 (2017) 273002. doi: 10.1088/1361-648X/aa680e
23. [23]
  S.P. Ong, W.D. Richards, A. Jain, et al., Comput. Mater. Sci. 68 (2013) 314–319. doi: 10.1016/j.commatsci.2012.10.028
24. [24]
  G. Kresse, J. Furthmüller, Phys. Rev. B 54 (1996) 11169. doi: 10.1103/PhysRevB.54.11169
25. [25]
  A.P. Thompson, H.M. Aktulga, R. Berger, et al., Comput. Phys. Commun. 271 (2022) 108171. doi: 10.1016/j.cpc.2021.108171
26. [26]
  V. Fung, P. Ganesh, B.G. Sumpter, Chem. Mater. 34 (2022) 4848–4855. doi: 10.1021/acs.chemmater.1c04252
27. [27]
  A. Vaswani, N. Shazeer, N. Parmar, et al., arXiv (2017) arXiv:1706.03762
28. [28]
  V. Fung, G. Hu, P. Ganesh, B.G. Sumpter, Nat. Commun. 12 (2021) 88. doi: 10.1038/s41467-020-20342-6
29. [29]
  L. Himanen, M.O. Jäger, E.V. Morooka, et al., Comput. Phys. Commun. 247 (2020) 106949. doi: 10.1016/j.cpc.2019.106949
30. [30]
  C. Chen, W. Ye, Y. Zuo, C. Zheng, S.P. Ong, Chem. Mater. 31 (2019) 3564–3572. doi: 10.1021/acs.chemmater.9b01294
31. [31]
  K. Tran, Z.W. Ulissi, Nat. Catal. 1 (2018) 696–703. doi: 10.1038/s41929-018-0142-1
32. [32]
  J. Lan, A. Palizhati, M. Shuaibi, et al., npj Comput. Mater. 9 (2023) 172. doi: 10.1038/s41524-023-01121-5
33. [33]
  X. Cao, W. Luo, H. Liu, RSC Adv. 14 (2024) 12235–12246. doi: 10.1039/d4ra00710g
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2. [2]
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