Skip to main content

Thank you for visiting 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.

Aiming beyond slight increases in accuracy

Owing to the diminishing returns of deep learning and the focus on model accuracy, machine learning for chemistry might become an endeavour exclusive to well-funded institutions and industry. Extending the focus to model efficiency and interpretability will make machine learning for chemistry more inclusive and drive methodological progress.

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

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


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

Fig. 1: More than accuracy.


  1. Ivakhnenko, A. G. The group method of data handling, a rival of the method of stochastic approximation. Sov. Automat. Contr. 13, 43–55 (1968).

    Google Scholar 

  2. Keith, J. A. et al. Combining machine learning and computational chemistry for predictive insights into chemical systems. Chem. Rev. 121, 9816–9872 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Probst, D. et al. Biocatalysed synthesis planning using data-driven learning. Nat. Commun. 13, 964 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Thompson, N. C., Greenewald, K., Lee, K. & Manso, G. F. Deep learning’s diminishing returns. IEEE Spectr. 58, 50–55 (2021).

    Article  Google Scholar 

  5. Ahmed, N. & Wahed, M. The de-democratization of AI: deep Learning and the compute divide in artificial intelligence research. Preprint at (2020).

  6. Jurowetzki, R., Hain, D., Mateos-Garcia, J. & Stathoulopoulos, K. The privatization of AI research(-ers): causes and potential consequences–from university-industry interaction to public research brain-drain? Preprint at (2021).

  7. Schwartz, R., Dodge, J., Smith, N. A. & Etzioni, O. Green AI. Commun. ACM 63, 54–63 (2020).

    Article  Google Scholar 

  8. Patterson, D. et al. The carbon footprint of machine learning training will plateau, then shrink. Preprint at (2022).

  9. Probst, D. Social and environmental impact of recent developments in machine learning on biology and chemistry research. Preprint at (2022).

  10. Scao, T. L. et al. BLOOM: a 176B-parameter open-access multilingual language model. Preprint at (2022).

Download references


The author thanks Pierre Vandergheynst for hosting them as a postdoctoral researcher at the École Polytechnique Fédérale de Lausanne.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Daniel Probst.

Ethics declarations

Competing interests

The author declares no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Probst, D. Aiming beyond slight increases in accuracy. Nat Rev Chem 7, 227–228 (2023).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


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