Citation: Jian Cao, Chang Liu, Danling Wang, Haichao Li, Lina Xu, Hongping Xiao, Shaoqi Zhan, Xiao He, Guoyong Fang. Machine learning potentials for property predictions of two-dimensional group-Ⅲ nitrides[J]. Acta Physico-Chimica Sinica, ;2026, 42(4): 100224. doi: 10.1016/j.actphy.2025.100224 shu

Machine learning potentials for property predictions of two-dimensional group-Ⅲ nitrides

Jian Cao ^{1, †} ,
Chang Liu ^{1, †} ,
Danling Wang ¹ ,
Haichao Li ¹ ,
Lina Xu ^1,, ,
Hongping Xiao ¹ ,
Shaoqi Zhan ² ,
Xiao He ^3,, ,
Guoyong Fang ^1,,

1.
College of Chemistry and Materials Engineering, Wenzhou University, Wenzhou 325035, Zhejiang Province, China
2.
Department of Chemistry - Ångström Laboratory, Uppsala University, Uppsala 75120, Sweden
3.
Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China

Corresponding author: Lina Xu, xulina@wzu.edu.cn Xiao He, xiaohe@phy.ecnu.edu.cn Guoyong Fang, fanggy@wzu.edu.cn
Received Date: 18 July 2025
Revised Date: 20 November 2025
Accepted Date: 23 November 2025

Due to the hexagonal structure, thermal stability, and wide bandgap, two-dimensional group-Ⅲ nitrides (h-BN, h-AlN, h-GaN and h-InN) show great promise for electronic and optoelectronic applications. Density functional theory (DFT) and classical molecular dynamics (MD) methods have advantages in calculation accuracy and scale respectively, but they are limited in the application of high-precision large-scale structure and performance research. Herein, we employ deep potential (DP) method to construct a high-precision machine learning potential (MLP) and systematically investigate the lattice dynamics, thermodynamic, mechanical, and thermal transport properties of two-dimensional Group Ⅲ nitrides. The DP method can achieve DFT accuracy in energy and atomic force predictions and accurately reproduce phonon dispersion and thermodynamic functions (free energy, heat capacity, entropy) across the 0–1200 K temperature range. MD simulations of uniaxial tensiles reveal distinct mechanical behavior differences among materials. h-BN exhibits high strength but brittle fracture characteristics, while h-AlN and h-GaN demonstrate good strength and ductility. h-InN shows relatively weak overall mechanical performance. Non-equilibrium MD simulations on thermal conductivity reveal significant length-dependent effects in h-BN and h-AlN, attributed to longer phonon mean free paths. Enhanced phonon scattering in h-GaN and h-InN results in lower thermal conductivities. These findings demonstrate that the DP method combines DFT accuracy with large-scale simulation capabilities can deepen understanding of structures and properties of two-dimensional Group Ⅲ nitrides and provide a computational framework and theoretical foundations for material design and device application.
- Group-Ⅲ nitride,
- Machine learning potential,
- Deep potential,
- Molecular dynamics,
- Thermal conductivity
1. [1]
  F. Yang, L. Jin, L. Sun, X. Ren, X. Duan, H. Cheng, Adv. Mater. 30 (2018) 1801891, https://doi.org/10.1002/adma.201801891. doi: 10.1002/adma.201801891
2. [2]
  S. Lv, S. Wang, L. Li, S. Xie, J. Yu, Y. Zhong, G. Wang, C. Liang, X. Xu, L. Zhang, Adv. Sci. 10 (2023) 2300780, https://doi.org/10.1002/advs.202300780. doi: 10.1002/advs.202300780
3. [3]
  Y. Guo, H. Zhu, Q. Wang, ACS Appl. Mater. Interfaces 11 (2018) 1033, https://doi.org/10.1021/acsami.8b17341. doi: 10.1021/acsami.8b17341
4. [4]
  S. Z. Butler, S. M. Hollen, L. Cao, Y. Cui, J. A. Gupta, H. R. Gutiérrez, T. F. Heinz, S. S. Hong, J. Huang, A. F. Ismach, et al., ACS Nano 7 (2013) 2898, https://doi.org/10.1021/nn400280c. doi: 10.1021/nn400280c
5. [5]
  A. H. Castro Neto, F. Guinea, N. M. R. Peres, K. S. Novoselov, A. K. Geim, Rev. Mod. Phys. 81 (2009) 109, https://doi.org/10.1002/adfm.202107499. doi: 10.1002/adfm.202107499
6. [6]
  D. Deng, K. S. Novoselov, Q. Fu, N. Zheng, Z. Tian, X. Bao, Nat. Nanotechnol. 11 (2016) 218, https://doi.org/10.1038/nnano.2015.340. doi: 10.1038/nnano.2015.340
7. [7]
  A. Gupta, T. Sakthivel, S. Seal, Prog. Mater. Sci. 73 (2015) 44, https://doi.org/10.1016/j.pmatsci.2015.02.002. doi: 10.1016/j.pmatsci.2015.02.002
8. [8]
  M. Xu, T. Liang, M. Shi, H. Chen, Chem. Rev. 113 (2013) 3766, https://doi.org/10.1021/cr300263a. doi: 10.1021/cr300263a
9. [9]
  Q. Cui, G. Qin, W. Wang, K. R. Geethalakshmi, A. Du, Q. Sun, Appl. Surf. Sci. 500 (2020) 143993, https://doi.org/10.1016/j.apsusc.2019.143993. doi: 10.1016/j.apsusc.2019.143993
10. [10]
  M. Q. Yang, N. Zhang, Y. J. Xu, ACS Appl. Mater. Interfaces 5 (2013) 1156, https://doi.org/10.1021/am3029798. doi: 10.1021/am3029798
11. [11]
  K. Xia, V. I. Artyukhov, L. Sun, J. Zheng, L. Jiao, B. I. Yakobson, Y. Zhang, Nano Res. 9 (2016) 2182, https://doi.org/10.1007/s12274-016-1107-9. doi: 10.1007/s12274-016-1107-9
12. [12]
  A. D. Oyedele, S. Yang, L. Liang, A. A. Puretzky, K. Wang, J. Zhang, P. R. Pudasaini, A. W. Ghosh, Z. Liu, C. M. Rouleau, et al., J. Am. Chem. Soc. 139 (2017) 14090, https://doi.org/10.1021/jacs.7b04865. doi: 10.1021/jacs.7b04865
13. [13]
  M. Corso, W. Auwa, M. Muntwiler, A. Tamai, Science 303 (2004) 217, https://doi.org/10.1126/science.1091979. doi: 10.1126/science.1091979
14. [14]
  J. Yu, L. Wang, Z. Hao, Y. Luo, C. Sun, J. Wang, Y. Han, B. Xiong, H. Li, Adv. Mater. 32 (2020) 1903407, https://doi.org/10.1002/adma.201903407. doi: 10.1002/adma.201903407
15. [15]
  Z. Wang, G. Wang, X. Liu, S. Wang, T. Wang, S. Zhang, J. Yu, G. Zhao, L. Zhang, J. Mater. Chem. C 9 (2021) 17201, https://doi.org/10.1039/D1TC04022G. doi: 10.1039/D1TC04022G
16. [16]
  B. Pécz, G. Nicotra, F. Giannazzo, R. Yakimova, A. Koos, A. Kakanakova-Georgieva, Adv. Mater. 22 (2020) 2006660, https://doi.org/10.1002/adma.202006660. doi: 10.1002/adma.202006660
17. [17]
  I. A. Aleksandrov, T. V. Malin, K. S. Zhuravlev, S. V. Trubina, S. B. Erenburg, B. Pécz, Y. V. Lebiadok, Appl. Surf. Sci. 515 (2020) 146001, https://doi.org/10.1016/j.apsusc.2020.146001. doi: 10.1016/j.apsusc.2020.146001
18. [18]
  M. E. Khan, M. Aamir, C. Ming, Y. -Y. Sun, Y. -H. Kim, FlatChem 51 (2025) 100846, https://doi.org/10.1016/j.flatc.2025.100846. doi: 10.1016/j.flatc.2025.100846
19. [19]
  E. Sandre, A. Pasturel, Mol. Simul. 20 (1997) 63, https://doi.org/10.1080/08927029708024168. doi: 10.1080/08927029708024168
20. [20]
  R. Vink, G. Barkema, W. Van der Weg, N. Mousseau, J. Non-Cryst. Solids 282 (2001) 248, https://doi.org/10.1016/S0022-3093(01)00342-8. doi: 10.1016/S0022-3093(01)00342-8
21. [21]
  J. Tersoff, Phys. Rev. B 37 (1988) 6991, https://doi.org/10.1103/physrevb.37.6991. doi: 10.1103/physrevb.37.6991
22. [22]
  W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz Jr., D. M. Ferguson, D. C. Spellmeyer, T. Fox, J. W. Caldwell, P. A. Kollman, J. Am. Chem. Soc. 117 (1995) 5179, https://doi.org/10.1021/ja955032e. doi: 10.1021/ja955032e
23. [23]
  L. D. Schuler, X. Daura, W. F. van Gunsteren, J. Comput. Chem. 22 (2001) 1205, https://doi.org/10.1002/jcc.1078. doi: 10.1002/jcc.1078
24. [24]
  K. Wan, J. He, X. Shi, Adv. Mater. 36 (2023) 2305758, https://doi.org/10.1002/adma.202305758. doi: 10.1002/adma.202305758
25. [25]
  D. Shvets, A. Gorbatov, Eur. Phys. J. Plus 139 (2024) 8685, https://doi.org/10.1140/epjp/s13360-024-05685-z. doi: 10.1140/epjp/s13360-024-05685-z
26. [26]
  J. Xia, Y. Zhang, B. Jiang, Chem. Soc. Rev. 54 (2025) 4790, https://doi.org/10.1039/d5cs00104h. doi: 10.1039/d5cs00104h
27. [27]
  S. Hwang, J. Jung, C. Hong, W. Jeong, S. Kang, S. Han, J. Am. Chem. Soc. 145 (2023) 35, https://doi.org/10.1021/jacs.3c06210. doi: 10.1021/jacs.3c06210
28. [28]
  J. Behler, M. Parrinello, Phys. Rev. Lett. 98 (2007) 146401, https://doi.org/10.1103/PhysRevLett.98.146401. doi: 10.1103/PhysRevLett.98.146401
29. [29]
  A. P. Bartók, M. C. Payne, R. Kondor, G. Csányi, Phys. Rev. Lett. 104 (2010) 136403, https://doi.org/10.1103/PhysRevLett.104.136403. doi: 10.1103/PhysRevLett.104.136403
30. [30]
  M. A. Caro, Phys. Rev. B 100 (2019) 024112, https://doi.org/10.1103/PhysRevB.100.024112. doi: 10.1103/PhysRevB.100.024112
31. [31]
  J. Byggmästar, K. Nordlund, F. Djurabekova, Phys. Rev. Mater. 6 (2022) 083801, https://doi.org/10.1103/PhysRevMaterials.6.083801. doi: 10.1103/PhysRevMaterials.6.083801
32. [32]
  A. Thompson, L. Swiler, C. Trott, S. Foiles, G. Tucker, J. Comput. Phys. 285 (2015) 316, https://doi.org/10.1016/j.jcp.2014.12.018. doi: 10.1016/j.jcp.2014.12.018
33. [33]
  I. S. Novikov, K. Gubaev, E. V. Podryabinkin, A. V. Shapeev, Mach. Learn. : Sci. Technol. 2 (2020) 025002, https://doi.org/10.1088/2632-2153/abc9fe. doi: 10.1088/2632-2153/abc9fe
34. [34]
  L. Zhang, J. Han, H. Wang, R. Car, W. E, Phys. Rev. Lett. 120 (2018) 143001, https://doi.org/10.1103/physrevlett.120.143001. doi: 10.1103/physrevlett.120.143001
35. [35]
  R. Drautz, Phys. Rev. B 23 (2019) 249901, https://doi.org/10.1103/PhysRevB.100.249901. doi: 10.1103/PhysRevB.100.249901
36. [36]
  H. Dong, X. Wu, Y. Shi, P. Ying, W. Zhou, K. Xu, T. Liang, S. Xiong, S. Chen, Y. Wang, et al., J. Appl. Phys. 135 (2024) 161101, https://doi.org/10.1063/5.0200833. doi: 10.1063/5.0200833
37. [37]
  G. Kresse, J. Furthmüller, Phys. Rev. B 54 (1996) 11169, https://doi.org/10.1103/PhysRevB.54.11169. doi: 10.1103/PhysRevB.54.11169
38. [38]
  A. D. Becke, J. Chem. Phys. 96 (1992) 2155, https://doi.org/10.1063/1.462066. doi: 10.1063/1.462066
39. [39]
  J. P. Perdew, K. Burke, M. Ernzerhof, Phys. Rev. Lett. 77 (1996) 3865, https://doi.org/10.1103/physrevlett.77.3865. doi: 10.1103/physrevlett.77.3865
40. [40]
  J. Ben, X. Liu, C. Wang, Y. Zhang, Z. Shi, Y. Jia, S. Zhang, H. Zhang, W. Yu, D. Li, X. Sun, Adv. Mater. 32 (2021) 2006761, https://doi.org/10.1002/adma.202006761. doi: 10.1002/adma.202006761
41. [41]
  M. A. Qaeed, K. Ibrahim, K. M. A. Saron, A. Salhin, Superlattices Microstruct. 64 (2013) 77, https://doi.org/10.1016/j.spmi.2013.08.015. doi: 10.1016/j.spmi.2013.08.015
42. [42]
  A. Costales, M. A. Blanco, A. Martí, J. Am. Chem. Soc. 124 (2002) 4116, https://doi.org/10.1021/ja017380o. doi: 10.1021/ja017380o
43. [43]
  K. He, X. Zhang, S. Ren, J. Sun, arXiv: 1512.03385, https://doi.org/10.48550/arXiv.1512.03385.
44. [44]
  H. Wang, L. Zhang, J. Han, W. E, Comput. Phys. Commun. 228 (2018) 178, https://doi.org/10.1016/j.cpc.2018.03.016. doi: 10.1016/j.cpc.2018.03.016
45. [45]
  Y. Du, C. Hao, Z. Meng, C. Wang, K. Peng, Y. Tian, W. Duan, L. Yang, P. Lin, S. Zhang, Comput. Mater. Sci. 242 (2024) 113078, https://doi.org/10.1016/j.commatsci.2024.113078. doi: 10.1016/j.commatsci.2024.113078
46. [46]
  W. Yu, Z. Zhang, X. Wan, J. Su, Q. Gui, H. Guo, H. -x. Zhong, J. Robertson, Y. Guo, Chem. Mater. 35 (2023) 6651, https://doi.org/10.1021/acs.chemmater.3c00524. doi: 10.1021/acs.chemmater.3c00524
47. [47]
  W. Liang, G. Lu, J. Yu, J. Mater. Sci. Technol. 75 (2021) 78, https://doi.org/10.1016/j.jmst.2020.09.040. doi: 10.1016/j.jmst.2020.09.040
48. [48]
  D. J. Evans, B. L. Holian, J. Chem. Phys. 83 (1985) 406, https://doi.org/10.1063/1.449071. doi: 10.1063/1.449071
49. [49]
  A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, et al., Comput. Phys. Commun. 271 (2022) 108171, https://doi.org/10.1016/j.cpc.2021.108171. doi: 10.1016/j.cpc.2021.108171
50. [50]
  T. Gao, H. He, Y. Liu, Z. Bian, Q. Chen, Q. Xie, Y. Liang, Q. Xiao, Surf. Interfaces 39 (2023) 102983, https://doi.org/10.1016/j.surfin.2023.102983. doi: 10.1016/j.surfin.2023.102983
51. [51]
  K. Dey, S. Shahriar, M. A. R. Anan, P. Malakar, M. M. Rahman, M. M. Chowdhury, RSC Adv. 14 (2024) 24483, https://doi.org/10.1039/d4ra04814h. doi: 10.1039/d4ra04814h
52. [52]
  B. Mortazavi, M. Makaremi, M. Shahrokhi, Z. Fan, T. Rabczuk, Carbon 137 (2018) 57, https://doi.org/10.1016/j.carbon.2018.04.090. doi: 10.1016/j.carbon.2018.04.090
53. [53]
  F. Müller-Plathe, J. Chem. Phys. 106 (1997) 6082, https://doi.org/10.1063/1.473271. doi: 10.1063/1.473271
54. [54]
  A. Chernatynskiy, S. R. Phillpot, Phys. Rev. B 82 (2010) 134301, https://doi.org/10.1103/physrevb.82.134301. doi: 10.1103/physrevb.82.134301
55. [55]
  M. Ozsipahi, S. Jean, A. Beskok, A. A. Wilson, Int. Commun. Heat Mass Transf. 163 (2025) 108658, https://doi.org/10.1016/j.icheatmasstransfer.2025.108658. doi: 10.1016/j.icheatmasstransfer.2025.108658
56. [56]
  Y. Yang, J. Ma, J. Yang, Y. Zhang, ACS Appl. Mater. Interfaces 14 (2022) 45742, https://doi.org/10.1021/acsami.2c14871. doi: 10.1021/acsami.2c14871
57. [57]
  Z. Li, S. Xiong, C. Sievers, Y. Hu, Z. Fan, N. Wei, H. Bao, S. Chen, D. Donadio, T. Ala-Nissila, J. Chem. Phys. 151 (2019) 234105, https://doi.org/10.1063/1.5132543. doi: 10.1063/1.5132543
58. [58]
  L. Chaput, A. Togo, I. Tanaka, G. Hug, Phys. Rev. B 84 (2011) 094302, https://doi.org/10.1103/physrevb.84.094302. doi: 10.1103/physrevb.84.094302
59. [59]
  S. K. Achar, L. Zhang, J. K. Johnson, J. Phys. Chem. C 125 (2021) 14874, https://doi.org/10.1021/acs.jpcc.1c01411. doi: 10.1021/acs.jpcc.1c01411
60. [60]
  J. Callaway, Phys. Rev. 113 (1959) 1046, https://doi.org/10.1103/PhysRev.113.1046. doi: 10.1103/PhysRev.113.1046
61. [61]
  G. A. Slack, J. Phys. Chem. Solids 34 (1973) 321, https://doi.org/10.1016/0022-3697(73)90092-9. doi: 10.1016/0022-3697(73)90092-9
62. [62]
  N. A. Sakharova, A. F. G. Pereira, J. M. Antunes, Nanomaterials 14 (2024) 1736, https://doi.org/10.3390/nano14211736. doi: 10.3390/nano14211736
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