Skip to main content
SpringerLink
Log in

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

  • Phase-Change Materials in Electronics and Photonics
  • Published:
MRS Bulletin Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. A. Pirovano, A.L. Lacaita, A. Benvenuti, F. Pellizzer, R. Bez, IEEE Trans. Electron Devices 51, 452 (2004).

    Article  Google Scholar 

  2. A.L. Lacaita, A. Redaelli, Microelectron. Eng. 109, 351 (2013).

    Article  CAS  Google Scholar 

  3. J. Choe, TechInsights (2017), http://www.techinsights.com/about-techinsights/overview/blog/intel-3D-xpoint-memory-die-removed-from-intel-optane-pcm.

  4. M. Wuttig, N. Yamada, Nat. Mater. 6, 824 (2007).

    Article  CAS  Google Scholar 

  5. D. Lencer, M. Salinga, M. Wuttig, Adv. Mater. 23, 2030 (2011).

    Article  CAS  Google Scholar 

  6. W. Kim, M. BrightSky, T. Masuda, N. Sosa, S. Kim, R. Bruce, F. Carta, G. Fraczak, H.-Y. Cheng, A. Ray, Y. Zhu, H.L. Lung, K. Suu, C. Lam, Proc. 2016 IEEE Int. Electron Devices Mtg. (IEDM), (IEEE, 2016), pp. 83–86.

  7. S. Kim, W. Kim, S.-W. Nam, MRS Bull. 44 (9), 710 (2019).

    Article  Google Scholar 

  8. F. Rao, K. Ding, Y. Zhou, Y. Zheng, M. Xia, S. Lv, Z. Song, S. Feng, I. Ronneberger, R. Mazzarello, W. Zhang, E. Ma, Science 358, 1423 (2017).

    Article  CAS  Google Scholar 

  9. G.W. Burr, R.M. Shelby, A. Sebastian, S. Kim, S. Kim, S. Sidler, K. Virwani, M. Ishii, P. Narayanan, A. Fumarola, L.L. Sanches, I. Boybat, M. Le Gallo, K. Moon, J. Woo, H. Hwang, Y. Leblebici, Adv. Phys. X 2, 89 (2016).

    Google Scholar 

  10. M. Wuttig, H. Bhaskaran, T. Taubner, Nat. Photonics 11, 465 (2017).

    Article  CAS  Google Scholar 

  11. S. Caravati, M. Bernasconi, T.D. Kühne, M. Krack, M. Parrinello, Appl. Phys. Lett. 91, 171906 (2007).

    Article  CAS  Google Scholar 

  12. J. Hegedüs, S.R. Elliott, Nat. Mater. 7, 399 (2008).

    Article  CAS  Google Scholar 

  13. J. Akola, R.O. Jones, Phys. Rev. B 76, 235201 (2007).

    Article  CAS  Google Scholar 

  14. W. Zhang, V.L. Deringer, R. Dronskowski, R. Mazzarello, E. Ma, M. Wuttig, MRS Bull. 40, 856 (2015).

  15. F. Zipoli, A. Curioni, New J. Phys. 15, 123006 (2013).

    Article  CAS  Google Scholar 

  16. V.L. Deringer, R. Dronskowski, M. Wuttig, Adv. Funct. Mater. 25, 6343 (2015).

    Article  CAS  Google Scholar 

  17. J. Behler, J. Chem. Phys. 145, 170901 (2016).

    Article  CAS  Google Scholar 

  18. A.P. Bartók, S. De, C. Poelking, N. Bernstein, J.R. Kermode, G. Csányi, M. Ceriotti, Sci. Adv. 3, e1701816 (2017).

    Article  Google Scholar 

  19. M.I. Jordan, T.M. Mitchell, Science 349, 255 (2015).

    Article  CAS  Google Scholar 

  20. A.P. Bartók, G. Csányi, Int. J. Quantum Chem. 115, 1051 (2015).

    Article  CAS  Google Scholar 

  21. J. Behler, M. Parrinello, Phys. Rev. Lett. 98, 146401 (2007).

    Article  CAS  Google Scholar 

  22. J. Behler, Angew. Chem. Int. Engl. 56, 12828 (2017).

    Article  CAS  Google Scholar 

  23. G.C. Sosso, G. Miceli, S. Caravati, J. Behler, M. Bernasconi, Phys. Rev. B 85, 174103 (2012).

    Article  CAS  Google Scholar 

  24. S. Gabardi, E. Baldi, E. Bosoni, D. Campi, S. Caravati, G.C. Sosso, J. Behler, M. Bernasconi, J. Phys. Chem. C 121, 23827 (2017).

    Article  CAS  Google Scholar 

  25. G.C. Sosso, D. Donadio, S. Caravati, J. Behler, M. Bernasconi, Phys. Rev. B 86, 104301 (2012).

    Article  CAS  Google Scholar 

  26. D. Campi, D. Donadio, G.C. Sosso, J. Behler, M. Bernasconi, J. Appl. Phys. 117, 015304 (2015).

    Article  CAS  Google Scholar 

  27. G.C. Sosso, V.L. Deringer, S.R. Elliott, G. Csányi, Mol. Simul. 44, 866 (2018).

    Article  CAS  Google Scholar 

  28. H. Weber, J. Orava, I. Kaban, J. Pries, A.L. Greer, Phys. Rev. Mater. 2, 093405 (2018).

    Article  CAS  Google Scholar 

  29. G.C. Sosso, J. Behler, M. Bernasconi, Phys. Status Solidi B 249, 1880 (2012).

    Article  CAS  Google Scholar 

  30. G.C. Sosso, J. Colombo, J. Behler, E. Del Gado, M. Bernasconi, J. Phys. Chem. B 118, 13621 (2014).

    Article  CAS  Google Scholar 

  31. S. Gabardi, S. Caravati, G.C. Sosso, J. Behler, M. Bernasconi, Phys. Rev. B 92, 054201 (2015).

    Article  CAS  Google Scholar 

  32. J.-Y. Raty, Phys. Status Solidi Rapid Res. Lett. 13, 1800590 (2019).

    Article  CAS  Google Scholar 

  33. G.C. Sosso, J. Chen, S.J. Cox, M. Fitzner, P. Pedevilla, A. Zen, A. Michaelides, Chem. Rev. 116, 7078 (2016).

    Article  CAS  Google Scholar 

  34. W. Zhang, R. Mazzarello, M. Wuttig, E. Ma, Nat. Rev. Mater. 4, 150 (2019).

    Article  CAS  Google Scholar 

  35. G.C. Sosso, G. Miceli, S. Caravati, F. Giberti, J. Behler, M. Bernasconi, J. Phys. Chem. Lett. 4, 4241 (2013).

    Article  CAS  Google Scholar 

  36. S. Gabardi, G.C. Sosso, J. Behler, M. Bernasconi, Faraday Discuss. 213, 287 (2019).

    Article  CAS  Google Scholar 

  37. G.C. Sosso, M. Salvalaglio, J. Behler, M. Bernasconi, M. Parrinello, J. Phys. Chem. C 119, 6428 (2015).

    Article  CAS  Google Scholar 

  38. F.C. Mocanu, G. Csányi, S.R. Elliott, J. Phys. Chem. B 122, 8998 (2018).

    Article  CAS  Google Scholar 

  39. H. Chan, B. Narayanan, M.J. Cherukara, F.G. Sen, K. Sasikumar, S.K. Gray, M.K.Y. Chan, S.K.R.S. Sankaranarayanan, J. Phys. Chem. C 123, 6941 (2019).

    Article  CAS  Google Scholar 

  40. L. Zhang, D.-Y. Lin, H. Wang, R. Car, W.E., Phys. Rev. Mater. 3, 023804 (2019).

    Article  CAS  Google Scholar 

  41. S. Hajinazar, J. Shao, A.N. Kolmogorov, Phys. Rev. B 95, 014114 (2017).

    Article  CAS  Google Scholar 

  42. B. Onat, E.D. Cubuk, B.D. Malone, E. Kaxiras, Phys. Rev. B 97, 094106 (2018).

    Article  CAS  Google Scholar 

  43. R. Kobayashi, D. Giofré, T. Junge, M. Ceriotti, W.A. Curtin, Phys. Rev. Mater. 1, 053604 (2017).

    Article  Google Scholar 

  44. E. Palumbo, P. Zuliani, M. Borghi, R. Annunziata, Solid State Electron. 133, 38 (2017).

    Article  CAS  Google Scholar 

  45. R.E. Simpson, P. Fons, A.V. Kolobov, T. Fukaya, M. Krbal, T. Yagi, J. Tominaga, Nat. Nanotechnol. 6, 501 (2011).

    Article  CAS  Google Scholar 

  46. M. Boniardi, J.E. Boschker, J. Momand, B.J. Kooi, A. Redaelli, R. Calarco, Phys. Status Solidi Rapid Res. Lett. 13, 1800634 (2019).

    Article  CAS  Google Scholar 

  47. M. Salinga, B. Kersting, I. Ronneberger, V.P. Jonnalagadda, X.T. Vu, M. Le Gallo, I. Giannopoulos, O. Cojocaru-Mirédin, R. Mazzarello, A. Sebastian, Nat. Mater. 17, 681 (2018).

    Article  CAS  Google Scholar 

  48. J.M. Wynn, P.V.C. Medeiros, A. Vasylenko, J. Sloan, D. Quigley, A.J. Morris, Phys. Rev. Mater. 1, 073001 (2017).

    Article  Google Scholar 

  49. B. Chen, G.H. ten Brink, G. Palasantzas, B.J. Kooi, Sci. Rep. 6, 39546 (2016).

    Article  CAS  Google Scholar 

  50. D.R. Cassar, A.C.P.L.F. de Carvalho, E.D. Zanotto, Acta Mater. 159, 249 (2018).

    Article  CAS  Google Scholar 

  51. C. Dreyfus, G.A. Dreyfus, J. Non Cryst. Solids 318, 63 (2003).

    Article  CAS  Google Scholar 

  52. N.M.A. Krishnan, S. Mangalathu, M.M. Smedskjaer, A. Tandia, H. Burton, M. Bauchy, J. Non Cryst. Solids 487, 37 (2018).

    Article  CAS  Google Scholar 

  53. L. Ward, S.C. O’Keeffe, J. Stevick, G.R. Jelbert, M. Aykol, C. Wolverton, Acta Mater. 159, 102 (2018).

  54. M.C. Onbaşlı, A. Tandia, J.C. Mauro, “Mechanical and Compositional Design of High-Strength Corning Gorilla Glass,” in Handbook of Materials Modeling, W. Andreoni, S. Yip, Eds. (Springer, Dordrecht, The Netherlands, 2018).

Download references

Acknowledgments

We acknowledge the contributions of several co-workers and, in particular, of J. Behler, who introduced us to the use of NN methods.

Author information

Authors and Affiliations

  1. Department of Chemistry and Centre for Scientific Computing, University of Warwick, UK

    G. C. Sosso

  2. Department of Materials Science, University of Milano-Bicocca, Italy

    M. Bernasconi

Authors
  1. G. C. Sosso
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Bernasconi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. C. Sosso.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sosso, G.C., Bernasconi, M. Harnessing machine learning potentials to understand the functional properties of phase-change materials. MRS Bulletin 44, 705–709 (2019). https://doi.org/10.1557/mrs.2019.202

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1557/mrs.2019.202

Search

Navigation