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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.

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Fig. 1: Data for atomistic machine learning.

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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).

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  1. Department of Chemistry, University of Oxford, Oxford, UK

    Chiheb Ben Mahmoud, John L. A. Gardner & Volker L. Deringer

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  1. Chiheb Ben Mahmoud
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  2. John L. A. Gardner
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Volker L. Deringer.

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

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

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  • Published: 12 June 2024

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

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