High-entropy carbide (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C mechanical properties prediction with the use of machine learning potential

封面

如何引用文章

全文:

开放存取 开放存取
受限制的访问 ##reader.subscriptionAccessGranted##
受限制的访问 订阅存取

详细

The six-component high-entropy carbide (HEC) (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C has been studied. The electronic structure was calculated by using the ab initio package VASP for a supercell with 512 atoms constructed by using special quasi-random structures. The artificial neural networks potential (ANN-potential) was obtained by deep machine learning. The quality of the ANN-potential was estimated by the value of the energies, forces, and virials standard deviations. The generated ANN-potential was used to analyze both a defect-free model of the specified alloy, with 4096 atoms, and for the first time a polycrystalline HEC model, with 4603 atoms, by using the LAMMPS classical molecular dynamics package. The simulation of uniaxial cell tension was carried out, the elasticity coefficients, the all-round compression modulus, the elasticity modulus, and Poisson’s ratio were determined. The obtained values are in good agreement with the experimental and calculated data, which indicates a good predictive ability of the generated ANN-potential.

全文:

受限制的访问

作者简介

N. Pikalova

Institute of Metallurgy, Ural Branch of the Russian Academy of Sciences

Email: rempel.imet@mail.ru
俄罗斯联邦, 620016 Ekaterinburg

I. Balyakin

Institute of Metallurgy, Ural Branch of the Russian Academy of Sciences; NANOTECH Centre, Ural Federal University

Email: rempel.imet@mail.ru
俄罗斯联邦, 620016 Ekaterinburg; 620002 Ekaterinburg

A. Yuryev

Institute of Metallurgy, Ural Branch of the Russian Academy of Sciences

Email: rempel.imet@mail.ru
俄罗斯联邦, 620016 Ekaterinburg

A. Rempel

Institute of Metallurgy, Ural Branch of the Russian Academy of Sciences

编辑信件的主要联系方式.
Email: rempel.imet@mail.ru

Academician of the RAS

俄罗斯联邦, 620016 Ekaterinburg

参考

  1. Yeh J.-W., Chen S.-K., Lin S.-J., Gan J.-Y., Chin T.-S., Shun T.-T., Tsau C.-H., Chang S.-Y. // Adv. Eng. Mater. 2004. V. 6. № 5. P. 299–303.
  2. https://doi.org/10.1002/adem.200300567
  3. Cantor B., Chang I.T.H., Knight P., Vincent A.J.B. // Mater. Sci. Eng., A. 2004. V. 375. P. 213–218. https://doi .org/10.1016/j.msea.2003.10.257
  4. Rost C.M., Sachet E., Borman T., Moballegh A., Dickey E.C., Hou D., Jones J.L., Curtarolo S., Maria J.-P. // Nat. Commun. 2015. V. 6. P. 8485–8492. https://doi .org/10.1038/ncomms9485
  5. Gild J., Zhang Y., Harrington T., Jiang S., Hu T., Quinn M.C., Mellor W.M., Zhou N., Vecchio K., Luo J. // Sci. Rep. 2016. V. 6. P. 37946. https://doi .org/10.1038/srep37946
  6. Han X., Girman V., Sedlák R., Dusza J., Castle E., Wang Y., Reece M., Zhang C. // J. Eur. Ceram. Soc. 2020. V. 40. № 7. P. 2709–2715. https://doi .org/10.1016/j.jeurceramsoc.2019.12.036
  7. Sarker P., Harrington T., Toher C., Oses C., Samiee M., Maria J.-P., Brenner D.W., Vecchio K.S., Curtarolo S. // Nat. Commun. 2018. V. 9. P. 4980. https://doi .org/10.1038/s41467-018-07160-7
  8. Gelchinski B.R., Balyakin I.A., Yuryev A.A., Rempel A.A. // Russ. Chem. Rev. 2022. V. 91. № 6. P. RCR5023. https://doi .org/10.1070/RCR5023
  9. Hohenberg P., Kohn W. // Phys. Rev. 1964. V. 136. № 3B. P. B864. https://doi .org/10.1103/PhysRev.136.B864
  10. Kohn W., Sham L.J. // Phys. Rev. 1965. V. 140. № 4A. P. A1133. https://doi .org/10.1103/PhysRev.140.A1133
  11. Zunger A., Wei S.-H., Ferreira L.G., Bernard J.E. // Phys. Rev. Lett. 1990. V. 65. № 3. P. 353–356. https://doi .org/10.1103/PhysRevLett.65.353
  12. Гельчинский Б.Р., Мирзоев А.А., Воронцов А.Г. Вычислительные методы микроскопической теории металлических расплавов и нанокластеров. М.: Физматлит, 2011. 200 с.
  13. Alder B.J., Wainwright T.E. // J. Chem. Phys. 1957. V. 27. P. 1208–1209. https://doi .org/10.1063/1.1743957
  14. Mishin Y. // Acta Mater. 2021. V. 214. P. 116980. https://doi .org/10.1016/j.actamat.2021.116980
  15. Alloy Theoretic Automated Toolkit (ATAT) // https://www.brown.edu/Departments/Engineering/Labs/avdw/atat/ (ссылка активна на 16.02.2024).
  16. The Vienna Ab initio Simulation Package: atomic scale materials modelling from first principles // https://www.vasp.at/ (ссылка активна на 16.02.2024).
  17. Wang H., Zhang L., Han J., E W. // Comput. Phys. Commun. 2018. V. 228. P. 178–184. https://doi .org/10.1016/j.cpc.2018.03.016
  18. Perdew J.P., Burke K., Ernzerhof M. // Phys. Rev. Lett. 1996. V. 77. № 18. P. 3865–3868. https://doi .org/10.1103/PhysRevLett.77.3865
  19. Zhang L., Han J., Wang H., Saidi W.A., Car R., E W. End-to-End Symmetry Preserving Inter-Atomic Potential Energy Model for Finite and Extended Systems. In: Advances in Neural Information Processing Systems. V. 31. Curran Associates, Inc., 2018. Montréal, Canada. P. 4436–4446.
  20. Thompson A.P., Aktulga H.M., Berger R., Bolintineanu D.S., Brown W.M., Crozier P.S., In ‘T Veld P.J., Kohlmeyer A., Moore S.G., Nguyen T.D., Shan R., Stevens M.J., Tranchida J., Trott C., Plimpton S.J. // Comput. Phys. Commun. 2022. V. 271. P. 10817. https://doi .org/10.1016/j.cpc.2021.108171
  21. Zhang Q., Zhang J., Li N., Chen W. // J. Appl. Phys. 2019. V. 126. P. 025101. https://doi .org/10.1063/1.5094580
  22. Ge H., Cui C., Song H., Tian F. // Metals. 2021. V. 11. № 9. P. 1399. https://doi .org/10.3390/met11091399
  23. Braic V., Vladescu A., Balaceanu M., Luculescu C., Braic M. // Surf. Coat. Technol. 2012. V. 211. P. 117–121. https://doi.org/10.1016/j.surfcoat.2011.09.033
  24. Chicardi E., García-Garrido C., Hernández-Saz J., Gotor F.J. // Ceram. Int. 2020. V. 46. № 13. P. 21421–21430. https://doi .org/10.1016/j.ceramint.2020.05.240
  25. Yang Y., Wang W., Gan G.-Y., Shi X.-F., Tang B.-Y. // Physica B: Condens. Matter. 2018. V. 550. P. 163–170. https://doi .org/10.1016/j.physb.2018.09.014
  26. Akrami S., Edalati P., Fuji M., Edalati K. // Mater. Sci. Eng., R. 2021. V. 146. P. 100644. https://doi .org/10.1016/j.mser.2021.100644
  27. Harrington T., Gild, J., Sarker P., Toher C., Rost C., Dippo O., McElfresh C., Kaufmann K., Marin E., Borowski L., Hopkins P., Luo J., Curtarolo S., Brenner D., Vecchio K. // Acta Mater. 2019. V. 166. P. 271–280. https://doi .org/10.1016/j.actamat.2018.12.054
  28. Moskovskikh D.O., Vorotilo S., Sedegov, A.S., Kuskov K.V., Bardasova K.V., Kiryukhantsev-Korneev P.V., Zhukovskyi M., Mukasyan A.S. // Ceram. Int. 2020. V. 46. P. 19008–19014. https://doi .org/10.1016/j.ceramint.2020.04.230
  29. Dai F.-Z., Wen B., Sun Y., Xiang H., Zhou Y. // J. Mater. Sci. Technol. 2020. V. 43. P. 168–174. https://doi .org/10.1016/j.jmst.2020.01.005
  30. Hirel P. // Comput. Phys. Commun. 2015. V. 197. P. 212–219. https://doi .org/10.1016/j.cpc.2015.07.012
  31. Zhang Y., Wang H., Chen W., Zeng J., Zhang L., Wang H., E W. // Comput. Phys. Commun. 2020. V. 253. P. 107206. https://doi .org/10.1016/j.cpc.2020.107206
  32. Lennard-Jones J.E. // Proc. Phys. Soc. 1931. V. 43. № 5. P. 461–482. https://doi .org/10.1088/0959-5309/43/5/301
  33. Becton M., Wang X. // Phys. Chem. Chem. Phys. 2015. V. 17. P. 21894–21901. https://doi .org/10.1039/c5cp03460d

补充文件

附件文件
动作
1. JATS XML
下载
2. Fig. 1. The value of the total energy of the system per atom for different SCS configurations (black dots) and random configurations (blue dots) and their average values.

下载 (171KB)
索引源数据
3. Fig. 2. Correlations between DeePMD and ab initio forces.

下载 (139KB)
索引源数据
4. Fig. 3. Lattice periods (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)C for different pressures of the system in comparison with the data of other authors: [21-23] (experiment), [20, 24] (calculation).

下载 (171KB)
索引源数据
5. Fig. 4. Graph of the dependence of the pressure of a polycrystalline system on the relative elongation (blue line) in comparison with a monocrystalline one (black line).

下载 (127KB)
索引源数据

版权所有 © Russian Academy of Sciences, 2024