Skip to main content
Log in

A self-driving lab for accelerated catalyst development

  • Research Briefing
  • Published:

From Nature Chemical Engineering

View current issue Submit your manuscript

A self-driving lab, called Fast-Cat, is developed for the rapid, autonomous Pareto-front mapping of homogeneous catalysts in high-pressure, high-temperature gas–liquid reactions. The efficacy of Fast-Cat was demonstrated in performing Pareto-front mappings of phosphorus-based ligands for the hydroformylation of olefins.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1: Fast-Cat’s experimental setup and closed-loop optimization cycle.

References

  1. Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019). This paper reports an artificial intelligence (AI)-guided retrosynthesis coupled with a robotically reconfigurable flow system.

    Article  CAS  PubMed  Google Scholar 

  2. Slattery, A. et al. Automated self-optimization, intensification, and scale-up of photocatalysis in flow. Science 383, eadj1817 (2024). This paper reports a closed-loop automated optimization of photocatalysis in flow.

    Article  CAS  PubMed  Google Scholar 

  3. Abolhasani, M. & Kumacheva, E. The rise of self-driving labs in chemical and materials sciences. Nat. Synth. 2, 483–492 (2023). A review article that presents advances in the field of self-driving labs.

    Article  ADS  Google Scholar 

  4. Bennett, J. A. & Abolhasani, M. Autonomous chemical science and engineering enabled by self-driving laboratories. Curr. Opin. Chem. Eng. 36, 100831 (2022). A review article that presents various methods and applications of self-driving labs.

    Article  Google Scholar 

  5. Daulton, S. et al. Differentiable expected hypervolume improvement for parallel multi-objective Bayesian optimization. Adv. Neural Inf. Process. Syst. 33, 9851–9864 (2020). This paper reports a multi-objective Bayesian optimization algorithm for maximizing the observed objective space.

    Google Scholar 

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Bennett, J. A. et al. Autonomous reaction Pareto-front mapping with a self-driving catalysis laboratory. Nat. Chem. Eng. https://doi.org/10.1038/s44286-024-00033-5 (2024).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

A self-driving lab for accelerated catalyst development. Nat Chem Eng 1, 206–207 (2024). https://doi.org/10.1038/s44286-024-00043-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44286-024-00043-3

  • Springer Nature America, Inc.

Navigation