FEATURES OF STRUCTURE AND PROPERTIES IN Ga70Bi30 DEMIXING MELT: EXPERIMENT AND MOLECULAR DYNAMICS METHOD

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

The temperature dependence of the dynamic viscosity of the demixing melt of critical composition Ga70Bi30 was measured by the method of damped torsional oscillations in the continuous cooling mode at a rate of 3 K/min from 1200 K to the critical temperature Tc and at a rate of 0.5 K/min from 600 K to Tc. It was found that the viscosity of this melt deviates from the Arrhenius dependence in the range of 14 K above the critical point. Molecular dynamics simulation of the Ga70Bi30 melt was carried out using the ANN potential. The initial training set was obtained by the ab initio MD method for a cell of 500 particles in the VASP package. The final training set was obtained using active machine learning in the DPGEN package. Based on these data, the ANN potential was trained in the DeepMD package. This potential was then used for simulations in the LAMMPS package for a system of 13,500 particles with a time step of 2.5 fs. The obtained potential was used to calculate the partial pair correlation functions, partial coordination numbers, diffusion coefficients and density of the melt of the critical composition Ga70Bi30. The partial radial distribution functions, density and diffusion coefficients were calculated in the isothermal mode in the range from 300 to 1300 K with a step of 200 K. The partial coordination numbers were obtained in the continuous cooling mode at a rate of 1011 K/s in the range of 400-800 K. An anomalous behavior of the partial coordination numbers was found, which begins at a temperature very close to the critical point. Anomalies were also found in the partial radial distribution functions near the critical temperature. No features were revealed in the temperature dependences of the calculated diffusion coefficients.

About the authors

V. V. Filippov

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

Ekaterinburg, Russia

I. A. Balyakin

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

Ekaterinburg, Russia

A. A. Yuryev

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

Email: yurev_anatoli@mail.ru
Ekaterinburg, Russia

B. R. Gelchinski

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

Ekaterinburg, Russia

References

  1. Predel. B. Bi-Ga (Bismuth-Gallium. In B-Ba – C-Zr. in: Landolt-Bornstein – Group IV Physical Chemistry. Vol. 5B. O. Madelung, Ed., Springer, 1992.
  2. Wignall G.D., Egelstaff P.A. Critical opalescence in binary liquid metal mixtures I. Temperature dependence. J. Phys. C: Solid State Phys. 1968. 1. P. 1088–1096. https://doi.org/10.1088/0022-3719/1/4/327
  3. Yagodin D.A., Filippov V.V., Popel P.S., Sidorov V.E., Son L.D. Density and ultrasound velocity in Ga-Bi melts. J. Phys. Conf. Ser. 2008. 98. P. 062019. https://doi.org/10.1088/1742-6596/98/6/062019.
  4. Vollmann J., Riedel D. The viscosity of liquid Bi–Ga alloys. J. Phys.: Condens. Matter. 1996. 8. P. 6175–6184. https://doi.org/10.1088/0953-8984/8/34/007
  5. Sklyarchuk V., Mudry S., Yakymovych A. Viscosity of Bi-Ga liquid alloys. J. Phys. Conf. Ser. 2008. 98. P. 062021. https://doi.org/10.1088/1742-6596/98/6/062021
  6. Adams P.D. Electrical resistivity of liquid binary alloys exhibiting a miscibility gap. Phys. Rev. Lett. 1970. 25. P. 1012–1014.
  7. Ginter G., Gasser J.G., Kleim R. The electrical resistivity of liquid bismuth, gallium and bismuth-gallium alloys. Phil. Mag. 1986. B 54. P. 543–552. https://doi.org/10.1080/13642818608236869
  8. Belashchenko D.K. Computer simulation of the properties of liquid metals: Gallium, lead, and bismuth. Russ. J. Phys. Chem. A. 2012. 86. P. 779–790. https://doi.org/10.1134/S0036024412050056
  9. Mokshin A.V., Khusnutdinoff R.M., Galimzyanov B.N., Brazhkin V.V. Extended short-range order determines the overall structure of liquid gallium. Phys. Chem. Chem. Phys. 2020. 22. P. 4122–4129. https://doi.org/10.1039/c9cp05219d
  10. Caspi E.N. et al. What is the structure of liquid Bismuth? J. Phys. Conf. Ser. 2012. 340. P. 012079. https://doi.org/10.1088/1742-6596/340/1/012079
  11. Kohn W., Sham L.J. Self-Consistent Equations Including Exchange and Correlation Effects. Phys. Rev. 1965. 140. P. A1133–A1138. https://doi.org/10.1103/PhysRev.140.A1133
  12. Mishin Y. Machine-learning interatomic potentials for materials science. Acta Mater. 2021. 214. P. 116980. https://doi.org/10.1016/j.actamat.2021.116980
  13. Behler J., Parrinello M. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces. Phys. Rev. Lett. 2007. 98. P. 146401. https://doi.org/10.1103/PhysRevLett.98.146401
  14. Balyakin I.A., Yuryev A.A., Filippov V.V., Gelchinski B.R. Viscosity of liquid gallium Neural network potential molecular dynamics and experimental stady. Comput. Mater. Sci. 2022. 215. P. 111802. https://doi.org/10.1016/j.commatsci.2022.111802
  15. Balyakin I.A., Yuryev A.A., Gelchinski B.R. Molecular Dynamics Simulation of the Immiscibility in Bi–Ga Melts. Russian Metallurgy (Metally). 2024. P. 1043–1047. https://doi.org/10.1134/S0036029524701994
  16. Kresse G., Furthmüller J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B. 1996. 54. P. 11169–11186. https://doi.org/10.1103/PhysRevB.54.11169
  17. Zhang Y., Wang Y., Chen W., Zeng J., Zhang L., Wang H., E W. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput. Phys. Commun. 2020. 253. P. 107206. https://doi.org/10.1016/j.cpc.2020.107206
  18. Wang H., Zhang L., Han J., E W. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics Comput. Phys. Commun. 2018. 228. P. 178–184. https://doi.org/10.1016/j.cpc.2018.03.016
  19. Thompson A.P., Actulga H.M., Berger R., Bolintineanu D.S., Brown W.M., Crozier P.S., 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. 271. P. 108171. https://doi.org/10.1016/j.cpc.2021.108171
  20. Filippov V.V., Uporov S.A., Bykov V.A. et al. An automated setup for measuring the viscosity of metal melts. Instrum Exp Tech. 2016. 59. P. 305–311. https://doi.org/10.1134/S0020441216010036
  21. Inui M., Takeda S., Uechi T. Ultrasonic Velocity and Density Measurement of Liquid Bi–Ga Alloys with Miscibility Gap Region. J. Physical Society of Japan. 1992. 61. P. 3203–3208. https://doi.org/10.1143/JPSJ.61.3203
  22. Darken L.S. Diffusion, Mobility and Their Interrelation through Free Energy in Binary Metallic Systems. Trans. AIME. 1948. 175. P. 184–201.
  23. Khairulin R.A., Stankus S.V., Sorokin A.L. Determination of the two-melt phase boundary and study of the binary diffusion in liquid Bi–Ga system with a miscibility gap. J. Non-Cryst. Solids. 2002. 297. P. 120–130.
  24. Menz W., Sauerwald F. Viskositatsmessungen XVIII: Die Viskositat der schmelzflussigen E-(Entmischungs-) systeme Ga-Cd, Ga-Hg, Ga-Bi. Z. Phys. Chem. 1966. 232. P. 134–137.

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2025 Russian Academy of Sciences

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).