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Machine learning speeds up search for surface structure

It is difficult to identify stable surface reconstructions of complex materials. Now a Monte Carlo sampling strategy is coupled with a machine learning interatomic potential that is iteratively improved via active learning during the search.

Inside the actual device, the surface structure is furthermore subject to changes when the external conditions (such as temperature or the concentration of gas-phase or liquid-phase species) change. The efficient sampling of these complex materials spaces using first-principles calculations (such as density functional theory) is hindered by the high computational cost of these methods. Now Xiaochen Du and colleagues—writing in Nature Computational Science4—have developed a computational framework called AutoSurfRecon to efficiently sample huge compositional and configurational spaces to identify the free-energy minima of surface reconstructions under specified working conditions (that is, the surface phase diagram).

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Fig. 1: Examples of surface reconstructions.

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Authors and Affiliations

  1. Aarhus Institute of Advanced Studies, Aarhus University, Aarhus, Denmark

    Mie Andersen

  2. Center for Interstellar Catalysis, Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark

    Mie Andersen

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  1. Mie Andersen
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Andersen, M. Machine learning speeds up search for surface structure.
Nat Comput Sci (2023). https://doi.org/10.1038/s43588-023-00575-3

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  • Published: 14 December 2023

  • DOI: https://doi.org/10.1038/s43588-023-00575-3

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