Skip to main content Accessibility help
×
Home

Harnessing machine learning potentials to understand the functional properties of phase-change materials

  • G.C. Sosso (a1) and M. Bernasconi (a2)

Abstract

The exploitation of phase-change materials (PCMs) in diverse technological applications can be greatly aided by a better understanding of the microscopic origins of their functional properties. Over the last decade, simulations based on electronic-structure calculations within density functional theory (DFT) have provided useful insights into the properties of PCMs. However, large simulation cells and long simulation times beyond the reach of DFT simulations are needed to address several key issues of relevance for the performance of devices. One way to overcome the limitations of DFT methods is to use machine learning (ML) techniques to build interatomic potentials for fast molecular dynamics simulations that still retain a quasi-ab initio accuracy. Here, we review the insights gained on the functional properties of the prototypical PCM GeTe by harnessing such interatomic potentials. Applications and future challenges of the ML techniques in the study of PCMs are also outlined.

Copyright

References

Hide All
1.Pirovano, A., Lacaita, A.L., Benvenuti, A., Pellizzer, F., Bez, R., IEEE Trans. Electron Devices 51, 452 (2004).
2.Lacaita, A.L., Redaelli, A., Microelectron. Eng. 109, 351 (2013).
4.Wuttig, M., Yamada, N., Nat. Mater. 6, 824 (2007).
5.Lencer, D., Salinga, M., Wuttig, M., Adv. Mater. 23, 2030 (2011).
6.Kim, W., BrightSky, M., Masuda, T., Sosa, N., Kim, S., Bruce, R., Carta, F., Fraczak, G., Cheng, H.-Y., Ray, A., Zhu, Y., Lung, H.L., Suu, K., Lam, C., Proc. 2016 IEEE Int. Electron Devices Mtg. (IEDM), (IEEE, 2016), pp. 8386.
7.Kim, S., Kim, W., Nam, S.-W., MRS Bull . 44 (9), 710 (2019).
8.Rao, F., Ding, K., Zhou, Y., Zheng, Y., Xia, M., Lv, S., Song, Z., Feng, S., Ronneberger, I., Mazzarello, R., Zhang, W., Ma, E., Science 358, 1423 (2017).
9.Burr, G.W., Shelby, R.M., Sebastian, A., Kim, S., Kim, S., Sidler, S., Virwani, K., Ishii, M., Narayanan, P., Fumarola, A., Sanches, L.L., Boybat, I., Le Gallo, M., Moon, K., Woo, J., Hwang, H., Leblebici, Y., Adv. Phys. X 2, 89 (2016).
10.Wuttig, M., Bhaskaran, H., Taubner, T., Nat. Photonics 11, 465 (2017).
11.Caravati, S., Bernasconi, M., Kühne, T.D., Krack, M., Parrinello, M., Appl. Phys. Lett. 91, 171906 (2007).
12.Hegedüs, J., Elliott, S.R., Nat. Mater. 7, 399 (2008).
13.Akola, J., Jones, R.O., Phys. Rev. B 76, 235201 (2007).
14.Zhang, W., Deringer, V.L., Dronskowski, R., Mazzarello, R., Ma, E., Wuttig, M., MRS Bull. 40, 856 (2015).
15.Zipoli, F., Curioni, A., New J. Phys. 15, 123006 (2013).
16.Deringer, V.L., Dronskowski, R., Wuttig, M., Adv. Funct. Mater. 25, 6343 (2015).
17.Behler, J., J. Chem. Phys. 145, 170901 (2016).
18.Bartók, A.P., De, S., Poelking, C., Bernstein, N., Kermode, J.R., Csányi, G., Ceriotti, M., Sci. Adv. 3, e1701816 (2017).
19.Jordan, M.I., Mitchell, T.M., Science 349, 255 (2015).
20.Bartók, A.P., Csányi, G., Int. J. Quantum Chem. 115, 1051 (2015).
21.Behler, J., Parrinello, M., Phys. Rev. Lett. 98, 146401 (2007).
22.Behler, J., Angew. Chem. Int. Engl. 56, 12828 (2017).
23.Sosso, G.C., Miceli, G., Caravati, S., Behler, J., Bernasconi, M., Phys. Rev. B 85, 174103 (2012).
24.Gabardi, S., Baldi, E., Bosoni, E., Campi, D., Caravati, S., Sosso, G.C., Behler, J., Bernasconi, M., J. Phys. Chem. C 121, 23827 (2017).
25.Sosso, G.C., Donadio, D., Caravati, S., Behler, J., Bernasconi, M., Phys. Rev. B 86, 104301 (2012).
26.Campi, D., Donadio, D., Sosso, G.C., Behler, J., Bernasconi, M., J. Appl. Phys. 117, 015304 (2015).
27.Sosso, G.C., Deringer, V.L., Elliott, S.R., Csányi, G., Mol. Simul. 44, 866 (2018).
28.Weber, H., Orava, J., Kaban, I., Pries, J., Greer, A.L., Phys. Rev. Mater. 2, 093405 (2018).
29.Sosso, G.C., Behler, J., Bernasconi, M., Phys. Status Solidi B 249, 1880 (2012).
30.Sosso, G.C., Colombo, J., Behler, J., Del Gado, E., Bernasconi, M., J. Phys. Chem. B 118, 13621 (2014).
31.Gabardi, S., Caravati, S., Sosso, G.C., Behler, J., Bernasconi, M., Phys. Rev. B 92, 054201 (2015).
32.Raty, J.-Y., Phys. Status Solidi Rapid Res. Lett. 13, 1800590 (2019).
33.Sosso, G.C., Chen, J., Cox, S.J., Fitzner, M., Pedevilla, P., Zen, A., Michaelides, A., Chem. Rev. 116, 7078 (2016).
34.Zhang, W., Mazzarello, R., Wuttig, M., Ma, E., Nat. Rev. Mater. 4 , 150 (2019).
35.Sosso, G.C., Miceli, G., Caravati, S., Giberti, F., Behler, J., Bernasconi, M., J. Phys. Chem. Lett. 4, 4241 (2013).
36.Gabardi, S., Sosso, G.C., Behler, J., Bernasconi, M., Faraday Discuss . 213, 287 (2019).
37.Sosso, G.C., Salvalaglio, M., Behler, J., Bernasconi, M., Parrinello, M., J. Phys. Chem. C 119, 6428 (2015).
38.Mocanu, F.C., Csányi, G., Elliott, S.R., J. Phys. Chem. B 122, 8998 (2018).
39.Chan, H., Narayanan, B., Cherukara, M.J., Sen, F.G., Sasikumar, K., Gray, S.K., Chan, M.K.Y., Sankaranarayanan, S.K.R.S., J. Phys. Chem. C 123, 6941 (2019).
40.Zhang, L., Lin, D.-Y., Wang, H., Car, R., , W.E., Phys. Rev. Mater. 3, 023804 (2019).
41.Hajinazar, S., Shao, J., Kolmogorov, A.N., Phys. Rev. B 95, 014114 (2017).
42.Onat, B., Cubuk, E.D., Malone, B.D., Kaxiras, E., Phys. Rev. B 97, 094106 (2018).
43.Kobayashi, R., Giofré, D., Junge, T., Ceriotti, M., Curtin, W.A., Phys. Rev. Mater. 1, 053604 (2017).
44.Palumbo, E., Zuliani, P., Borghi, M., Annunziata, R., Solid State Electron . 133, 38 (2017).
45.Simpson, R.E., Fons, P., Kolobov, A.V., Fukaya, T., Krbal, M., Yagi, T., Tominaga, J., Nat. Nanotechnol. 6, 501 (2011).
46.Boniardi, M., Boschker, J.E., Momand, J., Kooi, B.J., Redaelli, A., Calarco, R., Phys. Status Solidi Rapid Res. Lett. 13, 1800634 (2019).
47.Salinga, M., Kersting, B., Ronneberger, I., Jonnalagadda, V.P., Vu, X.T., Le Gallo, M., Giannopoulos, I., Cojocaru-Mirédin, O., Mazzarello, R., Sebastian, A., Nat. Mater. 17, 681 (2018).
48.Wynn, J.M., Medeiros, P.V.C., Vasylenko, A., Sloan, J., Quigley, D., Morris, A.J., Phys. Rev. Mater. 1, 073001 (2017).
49.Chen, B., ten Brink, G.H., Palasantzas, G., Kooi, B.J., Sci. Rep. 6, 39546 (2016).
50.Cassar, D.R., de Carvalho, A.C.P.L.F., Zanotto, E.D., Acta Mater . 159, 249 (2018).
51.Dreyfus, C., Dreyfus, G.A., J. Non Cryst. Solids 318, 63 (2003).
52.Krishnan, N.M.A., Mangalathu, S., Smedskjaer, M.M., Tandia, A., Burton, H., Bauchy, M., J. Non Cryst. Solids 487, 37 (2018).
53.Ward, L., O’Keeffe, S.C., Stevick, J., Jelbert, G.R., Aykol, M., Wolverton, C., Acta Mater. 159, 102 (2018).
54.Onbaşlı, M.C., Tandia, A., Mauro, J.C., “Mechanical and Compositional Design of High-Strength Corning Gorilla Glass,” in Handbook of Materials Modeling, Andreoni, W., Yip, S., Eds. (Springer, Dordrecht, The Netherlands, 2018).

Keywords

Harnessing machine learning potentials to understand the functional properties of phase-change materials

  • G.C. Sosso (a1) and M. Bernasconi (a2)

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed