Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

Shifting computational boundaries for complex organic materials

Nature Physics volume 17, pages 152–154 (2021)Cite this article

Subjects

Methodology adapted from data science sparked the field of materials informatics, and materials databases are at the heart of it. Applying artificial intelligence to these databases will allow the prediction of the properties of complex organic crystals.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Organic molecular crystals and metal–organic frameworks typically have highly complex unit cells with hundreds of atoms present.
Fig. 2: Machine learning techniques are designed for data regression and typically fail when it comes to extrapolation.

References

  1. Gaita-Ariño, A., Luis, F., Hill, S. & Coronado, E. Nat. Chem. 11, 301–309 (2019).

    Article  Google Scholar 

  2. Shimizu, Y., Miyagawa, K., Kanoda, K., Maesato, M. & Saito, G. Phys. Rev. Lett. 91, 107001 (2003).

    Article  ADS  Google Scholar 

  3. Layfield, R. A. Organometallics 33, 1084–1099 (2014).

    Article  Google Scholar 

  4. Schleder, G. R. et al. J. Phys. Mater. 2, 032001 (2019).

    Article  Google Scholar 

  5. Himanen, L., Geurts, A., Foster, A. S. & Rinke, P. Adv. Sci. 6, 1900808 (2019).

    Article  Google Scholar 

  6. Chmiela, S., Sauceda, H. E., Müller, K.-R. & Tkatchenko, A. Nat. Commun. 9, 3887 (2018).

    Article  ADS  Google Scholar 

  7. Geilhufe, R. M. & Olsthoorn, B. Phys. Rev. B 102, 205134 (2020).

    Article  ADS  Google Scholar 

  8. Huan, T. D. et al. J. Appl. Phys. 128, 171104 (2020).

    Article  ADS  Google Scholar 

  9. Borysov, S. S., Olsthoorn, B., Gedik, M. B., Geilhufe, R. M. & Balatsky, A. V. npj Comput. Mater. 4, 46 (2018).

    Article  ADS  Google Scholar 

  10. Geilhufe, R. M., Borysov, S. S., Kalpakchi, D. & Balatsky, A. V. Phys. Rev. Mater. 2, 024802 (2018).

    Article  Google Scholar 

  11. Geilhufe, R. M., Bouhon, A., Borysov, S. S. & Balatsky, A. V. Phys. Rev. B 95, 041103 (2017).

    Article  ADS  Google Scholar 

  12. Geilhufe, R. M., Borysov, S. S., Bouhon, A. & Balatsky, A. V. Sci. Rep. 7, 7298 (2017).

    Article  ADS  Google Scholar 

  13. Zhang, L., Chen, Z., Su, J. & Li, J. Renew. Sustain. Energy Rev. 107, 554–567 (2019).

    Article  Google Scholar 

  14. Hellsvik, J., Pérez, R. D., Geilhufe, R. M., Månsson, M. & Balatsky, A. V. Phys. Rev. Mater. 4, 024409 (2020).

    Article  Google Scholar 

  15. Geilhufe, R. M. et al. Phys. Status Solidi Rapid Res. Lett. 12, 1800293 (2018).

    Article  ADS  Google Scholar 

  16. Fonseca Guerra, C. F., Snijders, J. G., te Velde, G. & Baerends, E. J. Theor. Chem. Acc. 99, 391–403 (1998).

    Google Scholar 

  17. Gražulis, S. et al. Nucleic Acids Res. 40, D420–D427 (2012).

    Article  Google Scholar 

  18. Borysov, S. S., Geilhufe, R. M. & Balatsky, A. V. PLoS ONE 12, e0171501 (2017).

    Article  Google Scholar 

  19. Huan, T. D., Mannodi-Kanakkithodi, A. & Ramprasad, R. Phys. Rev. B 92, 014106 (2015).

    Article  ADS  Google Scholar 

  20. Grisafi, A. & Ceriotti, M. J. Chem. Phys. 151, 204105 (2019).

    Article  ADS  Google Scholar 

  21. Rupp, M., Tkatchenko, A., Müller, K.-R. & von Lilienfeld, O. A. Phys. Rev. Lett. 108, 058301 (2012).

    Article  ADS  Google Scholar 

  22. Schütt, K. et al. Adv. Neural Inf. Process. Syst. 30, 991–1001 (2017).

    Google Scholar 

  23. Bartók, A. P., Kondor, R. & Csányi, G. Phys. Rev. B 87, 184115 (2013).

    Article  ADS  Google Scholar 

  24. Tsubaki, M. & Mizoguchi, T. Phys. Rev. Lett. 125, 206401 (2020).

    Article  ADS  Google Scholar 

  25. Gong, S. et al. Phys. Rev. B 100, 184103 (2019).

    Article  ADS  Google Scholar 

  26. Kingma, D. P. & Welling, M. Preprint at https://arxiv.org/abs/1312.6114 (2013).

  27. Goodfellow, I. et al. Adv. Neural Inf. Process. Syst. 27, 2672–2680 (2014).

    Google Scholar 

  28. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Science 361, 360–365 (2018).

    Article  ADS  Google Scholar 

  29. Jackson, N. E., Webb, M. A. & de Pablo, J. J. Curr. Opin. Chem. Eng. 23, 106–114 (2019).

    Article  Google Scholar 

  30. Simon, A. J. et al. Nat. Chem. 11, 204–212 (2019).

    Article  Google Scholar 

  31. Huan, T. D. et al. Sci. Data 3, 160012 (2016).

    Article  Google Scholar 

  32. Ramakrishnan, R., Dral, P. O., Rupp, M. & von Lilienfeld, O. A. Sci. Data 1, 140022 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

We are grateful for collaboration and discussions with J. Hellsvik and S. S. Borysov. We acknowledge funding from the ERC synergy grant ‘HERO’ (number 810451), the University of Connecticut, VILLUM FONDEN via the Centre of Excellence for Dirac Materials (grant number 11744), the Knut and Alice Wallenberg Foundation (2019.0068) and the Vetenskapsrådet (number 2017.03997). We acknowledge computational resources from the Swedish National Infrastructure for Computing (SNIC) at the Centre for High Performance Computing (PDC), the High Performance Computing Centre North (HPC2N) and the Uppsala Multidisciplinary Centre for Advanced Computational Science (UPPMAX).

Author information

Authors and Affiliations

  1. Nordita, KTH Royal Institute of Technology and Stockholm University, Stockholm, Sweden

    R. Matthias Geilhufe, Bart Olsthoorn & Alexander V. Balatsky

  2. Department of Physics, University of Connecticut, Storrs, CT, USA

    Alexander V. Balatsky

Authors
  1. R. Matthias Geilhufe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bart Olsthoorn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander V. Balatsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to R. Matthias Geilhufe or Alexander V. Balatsky.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Geilhufe, R.M., Olsthoorn, B. & Balatsky, A.V. Shifting computational boundaries for complex organic materials. Nat. Phys. 17, 152–154 (2021). https://doi.org/10.1038/s41567-020-01135-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41567-020-01135-6

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing