Abstract
The melting point of the high-entropy Cantor alloy CoCrFeMnNi was calculated by the classical molecular dynamics method. Interatomic potential as a set of artificial neural networks was used for simulation of this type for the first time. Neural network coefficients were optimized using machine learning technique with ab initio molecular dynamics data. Ab initio molecular dynamics simulation was carried out for a wide temperature range using the same initial crystalline state. The initial state for ab initio simulations was a special quasi-random structure optimized on pairs of the nearest neighbors. The two-phase method based on the movement of phase boundary in a crystal–melt system was used to calculate the melting point. It should be noted that, although the training set did not contain explicit two-phase configurations, the computed melting point proved to be in a satisfactory agreement with available experimental data. Thus, the melting point of the high-entropy CoCrFeMnNi alloy was calculated without the use of empirical data.
Similar content being viewed by others
REFERENCES
Otto, F., Yang, Y., Bei, H., and George, E.P., Acta Mater., 2013, vol. 61, no. 7, pp. 2628–2638. https://doi.org/10.1016/j.actamat.2013.01.042
Zhang, W., Tang, R., Yang, Z.B., Liu, C.H., Chang, H., Yang, J.J., Liao, J.L., Yang, Y.Y., and Liu, N., J. Nucl. Mater., 2018, vol. 512, pp. 15–24. https://doi.org/10.1016/j.jnucmat.2018.10.001
Tunes, M.A., Vishnyakov, V.M., and Donnelly, S.E., Thin Solid Films, 2018, vol. 649, pp. 115–120. https://doi.org/10.1016/j.tsf.2018.01.041
Kareer, A., Waite, J.C., Li, B., Couet, A., Armstrong, D.E.J., and Wilkinson, A.J., J. Nucl. Mater., 2019, vol. 526, art. no. 151744. https://doi.org/10.1016/j.jnucmat.2019.151744
Popescu, G., Ghiban, B., Popescu, C.A., Rosu, L., Truscă, R., Carcea, I., Soare, V., Dumitrescu, D., Constantin, I., Olaru, M.T., and Carlan, B.A., IOP Conf. Ser.: Mater. Sci. Eng., 2018, vol. 400, no. 2, art. no. 022049. https://doi.org/10.1088/1757-899X/400/2/022049
Castro, D., Jaeger, P., Baptista, A.C., and Oliveira, J.P., Metals, 2021, vol. 11, no. 4, art. no. 648. https://doi.org/10.3390/met11040648
Yuan, Y., Wu, Y., Yang, Z., Liang, X., Lei, Zh., Huang, H., Wang, H., Liu, X., An, K., Wu, W., Lu, Zh., Mater. Res. Lett., 2019, vol. 7, no. 6, pp. 225–231. https://doi.org/10.1080/21663831.2019.1584592
Gludovatz, B., Hohenwarter, A., Catoor, D., Chang, E.H., George, E.P., and Ritchie, R.O., Science, 2014, vol. 345, no. 6201, pp. 1153–1158. https://doi.org/10.1126/science.1254581
Senkov, O.N., Miracle, D.B., Chaput, K.J., and Couzinie, J.-P., J. Mater. Res., 2018, vol. 33, no. 19, pp. 3092–3128. https://doi.org/10.1557/jmr.2018.153
Li, K., Khanna, R., Zhang, J., Li, G., Li, H., Jiang, C., Sun, M., Wang, Z., Bu, Y., Bouhadja, M., Liu, Z., and Barati, M., J. Mol. Liq., 2019, vol. 290, art. no. 111204. https://doi.org/10.1016/j.molliq.2019.111204
Etesami, S.A. and Asadi, E., J. Phys. Chem. Solids, 2018, vol. 112, pp. 61–72. https://doi.org/10.1016/j.jpcs.2017.09.001
Baskes, M.I., Phys. Rev. Lett., 1987, vol. 59, no. 23, p. 2666. https://doi.org/10.1103/PhysRevLett.59.2666
Hong, Q.-J. and Van De Walle, A., Phys. Rev. B, 2015, vol. 92, art. no. 20104. https://doi.org/10.1103/PhysRevB.92.020104
Mishin, Y., Acta Mater., 2021, vol. 214, art. no. 116980. https://doi.org/10.1016/j.actamat.2021.116980
Zunger, A., Wei, S.-H., Ferreira, L.G., and Bernard, J.E., Phys. Rev. Lett., 1990, vol. 65, no. 3, pp. 353–356. https://doi.org/10.1103/PhysRevLett.65.353
Van De Walle, A., Tiwary, P., De Jong, M., Olmsted, D.L., Asta, M., Dick, A., Shin, D., Wang, Y., Chen, L.-Q., and Liu, Z.-K., CALPHAD, 2013, vol. 42, pp. 13–18. https://doi.org/10.1016/j.calphad.2013.06.006
Zaddach, A.J., Niu, C., Koch, C.C., and Irving, D.L., JOM, 2013, vol. 65, no. 12, pp. 1780–1789. https://doi.org/10.1007/s11837-013-0771-4
Gao, M.C., Niu, C., Jiang, C., and Irving, D.L., in: High-Entropy Alloys, Gao, M., Yeh, J.W., Liaw, P., Zhang, Y., Eds., Cham: Springer, 2016, pp. 333–368.
Zhang, Y. and Maginn, E.J., J. Chem. Phys., 2012, vol. 136, no. 14, p. 144116. https://doi.org/10.1063/1.3702587
Luo, S.-N., Strachan, A., and Swift, D.C., J. Chem. Phys., 2004, vol. 120, no. 24, pp. 11640–11649. https://doi.org/10.1063/1.1755655
Phillpot, S.R., Lutsko, J.F., Wolf, D., and Yip, S., Phys. Rev. B, 1989, vol. 40, no. 5, p. 2831. https://doi.org/10.1103/PhysRevB.40.2831
Morris, J.R., Wang, C.Z., Ho, K.M., and Chan, C.T., Phys. Rev. B, vol. 49, no. 5. https://doi.org/10.1103/PhysRevB.49.3109
Hoover, W.G. and Ree, F.H., J. Chem. Phys., 2004, vol. 47, no. 12, pp. 4873–4878. https://doi.org/10.1063/1.1701730
Frenkel, D. and Ladd, A.J.C., J. Chem. Phys., 1998, vol. 81, no. 7, pp. 3188–3193. https://doi.org/10.1063/1.448024
Grochola, G., J. Chem. Phys., 2004, vol. 120, no. 5, pp. 2122–2126. https://doi.org/10.1063/1.1637575
Balyakin, I.A., Yuryev, A.A., Gelchinski, B.R., and Rempel, A.A., J. Phys. Condens. Matter, 2020, vol. 32, no. 21, art. no. 214006. https://doi.org/10.1088/1361‑648X/ab6f87
Cantor, B., Chang, I.T.H., Knight, P., and Vincent, A.J.B., Mater. Sci. Eng., A, 2004, vols. 375–377, nos. 1–2, pp. 213–218. https://doi.org/10.1016/j.msea.2003.10.257
Van de Walle, A., Asta, M., and Ceder, G., CALPHAD, 2002, vol. 26, no. 4, pp. 539–553. https://doi.org/10.1016/S0364-5916(02)80006-2
Kresse, G. and Furthmüller, J., Phys. Rev. B, 1996, vol. 54, no. 16, p. 11169. https://doi.org/10.1103/PhysRevB.54.11169
Perdew, J.P., Burke, K., and Ernzerhof, M., Phys. Rev. Lett., 1996, vol. 77, no. 18, p. 3865. https://doi.org/10.1103/PhysRevLett.77.3865
Wang, H., Zhang, L., and Han, J., E W., Comput. Phys. Commun., 2018, vol. 228, pp. 178–184. https://doi.org/10.1016/j.cpc.2018.03.016
Zhang, L., Han, J., Wang, H., Saidi, W.A., Car, R., and E, W., 32nd Conference on Neural Information Proces-sing Systems (NeurIPS 2018), Montréal, Canada, pp. 4441–4451.
Larsen, P.M., Schmidt, S., and Schiøtz, J.R., Model. Simul. Mater. Sci. Eng., 2016, vol. 24, no. 5. https://doi.org/10.1088/0965-0393/24/5/055007
Stukowski, A., Model. Simul. Mater. Sci. Eng., 2009, vol. 18, no. 1, art. no. 015012. https://doi.org/10.1088/0965-0393/18/1/015012
Vaidya, M., Trubel, S., Murty, B.S., Wilde, G., and Divinski, S.V., J. Alloys Compd., 2016, vol. 688, pp. 994–1001. https://doi.org/10.1016/j.jallcom.2016.07.239
Galvin, C.O.T., Grimes, R.W., and Burr, P.A., Comput. Mater. Sci., 2021, vol. 186, p. 110016. https://doi.org/10.1016/j.commatsci.2020.110016
ACKNOWLEDGMENTS
The study was carried out using the Uran supercomputer of the Institute of Mathematics and Mechanics, Ural Branch of the Russian Academy of Sciences.
Funding
The study was supported by the Russian Foundation for Basic Research within the framework of the research project no. 20-33-90171.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declare that they have no conflicts of interest.
Additional information
Translated by Z. Svitanko
Rights and permissions
About this article
Cite this article
Balyakin, I.A., Rempel, A.A. Atomistic Calculation of the Melting Point of the High-Entropy Cantor Alloy CoCrFeMnNi. Dokl Phys Chem 502, 11–17 (2022). https://doi.org/10.1134/S0012501622010018
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0012501622010018