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The perils of machine learning in designing new chemicals and materials

Nature Machine Intelligence volume 4, pages 314–315 (2022)Cite this article

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To the Editor — Machine learning is poised to revolutionize practice in chemistry and materials science. Already, machine learning is being used to find new pharmaceutical compounds, including in the fight against the COVID-19 pandemic. This holds great promise for the future, but also great peril. Right now, too little attention is being paid to the downside, as pointed out in a recent Comment by Urbina et al.1.

Less obvious are the disadvantages that this new power of machine intelligence brings. Just as the machine-learning analysis of bodily fluids might be used to develop medicines targeted to an individual, it may also be used to engineer viruses or toxins that might infect only certain people depending on their genes — a truly frightening prospect. Similarly, while machine learning might help to invent improved materials, such as biodegradable plastics and longer-lasting batteries, it is also easy to imagine how it might be used to design a tasteless compound that could be used to poison a community’s water supply.

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Fig. 1: Illustration of the number of possible material and chemical combinatorial possibilities.

References

  1. Urbina, F. et al. Nat. Mach. Intell. 4, 189–191 (2022).

    Article  Google Scholar 

  2. Krausmann, F. et al. Ecol. Econ. 68, 2696–2705 (2009).

    Article  Google Scholar 

  3. Pelley, J. Number of chemicals in commerce has been vastly underestimated. Chemical & Engineering News (12 February 2020).

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Acknowledgements

We would like to acknowledge the Stanford Human AI Institute (HAI) and HAI Ethics Panel for bringing the various issues to our attention; K. Geiser (Massachusetts) and others for sharing their experiences; and V. Shankar (Stanford) for his analysis.

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Authors and Affiliations

  1. SLAC National Accelerator Laboratory, Menlo Park, CA, USA

    Sadasivan Shankar

  2. Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA

    Sadasivan Shankar

  3. Department of Chemistry, Stanford University, Stanford, CA, USA

    Richard N. Zare

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  1. Sadasivan Shankar
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  2. Richard N. Zare
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Correspondence to Sadasivan Shankar or Richard N. Zare.

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The authors declare no competing interests.

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Shankar, S., Zare, R.N. The perils of machine learning in designing new chemicals and materials. Nat Mach Intell 4, 314–315 (2022). https://doi.org/10.1038/s42256-022-00481-9

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