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:

Data as the next challenge in atomistic machine learning

As machine learning models are becoming mainstream tools for molecular and materials research, there is an urgent need to improve the nature, quality, and accessibility of atomistic data. In turn, there are opportunities for a new generation of generally applicable datasets and distillable models.

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

Access options

Buy this article

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

Fig. 1: Data for atomistic machine learning.

References

  1. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Nature 559, 547–555 (2018).

    Article  Google Scholar 

  2. Zhou, Y., Zhang, W., Ma, E. & Deringer, V. L. Nat. Electron. 6, 746–754 (2023).

    Article  Google Scholar 

  3. Merchant, A. et al. Nature 624, 80–85 (2023).

    Article  Google Scholar 

  4. Behler, J. Angew. Chem. Int. Ed. 56, 12828–12840 (2017).

    Article  Google Scholar 

  5. Unke, O. T. et al. Chem. Rev. 121, 10142–10186 (2021).

    Article  Google Scholar 

  6. Deringer, V. L. et al. Chem. Rev. 121, 10073–10141 (2021).

    Article  Google Scholar 

  7. Batzner, S. et al. Nat. Commun. 13, 2453 (2022).

    Article  Google Scholar 

  8. Ko, T. W. & Ong, S. P. Nat. Comput. Sci. 3, 998–1000 (2023).

    Article  Google Scholar 

  9. Gardner, J. L. A., Baker, K. T. & Deringer, V. L. Mach. Learn. Sci. Technol. 5, 015003 (2024).

    Article  Google Scholar 

  10. Morrow, J. D. & Deringer, V. L. J. Chem. Phys. 157, 104105 (2022).

    Article  Google Scholar 

  11. Zhang, D. et al. Preprint at https://arxiv.org/abs/2312.15492 (2023).

  12. Noé, F., Olsson, S., Köhler, J. & Wu, H. Science 365, eaaw1147 (2019).

    Article  Google Scholar 

  13. Oganov, A. R., Pickard, C. J., Zhu, Q. & Needs, R. J. Nat. Rev. Mater. 4, 331–348 (2019).

    Article  Google Scholar 

  14. Batatia, I. et al. Preprint at https://arxiv.org/abs/2401.00096 (2024).

  15. Focassio, B., Freitas, L. P. M. & Schleder, G. R. Preprint at http://arxiv.org/abs/2403.04217 (2024).

  16. Unke, O. T. & Meuwly, M. J. Chem. Theory Comput. 15, 3678–3693 (2019).

    Article  Google Scholar 

  17. Artrith, N. et al. Nat. Chem. 13, 505–508 (2021).

    Article  Google Scholar 

  18. Tedersoo, L. et al. Sci. Data 8, 192 (2021).

    Article  Google Scholar 

Download references

Acknowledgements

We thank Z. Faure Beaulieu for useful discussions. J.L.A.G. acknowledges a UKRI Linacre - The EPA Cephalosporin Scholarship, support from an EPSRC DTP award (grant no. EP/T517811/1), and from the Department of Chemistry, University of Oxford. V.L.D. acknowledges a UK Research and Innovation Frontier Research grant (grant no. EP/X016188/1).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the writing of this Comment.

Corresponding author

Correspondence to Volker L. Deringer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Computational Science thanks Ekin Cubuk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ben Mahmoud, C., Gardner, J.L.A. & Deringer, V.L. Data as the next challenge in atomistic machine learning. Nat Comput Sci 4, 384–387 (2024). https://doi.org/10.1038/s43588-024-00636-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43588-024-00636-1

Search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

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