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Machine learning reveals the complexity of dense amorphous silicon

Transitions between amorphous forms of solids and liquids are difficult to study. Machine learning has now provided fresh insight into pressure-induced transformations of amorphous silicon, opening the way to studies of other systems.
  1. Paul F. McMillan

    1. Paul F. McMillan is in the Department of Chemistry, University College London, London WC1H 0AJ, UK.

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Machine-learning approaches are being developed to produce accurate simulations of the structure and chemical bonding of disordered solids and liquids, modelling a sufficient number of atoms to enable direct comparison with experimental data. Writing in Nature, Deringer et al.1 report their use of this approach to probe the structure of amorphous silicon under compression, as the element transforms from semiconducting to metallic states. Their work demonstrates that the structural transformations of amorphous forms of materials can take place much more gradually than those between crystalline phases, and can involve the formation of nanostructured domains and localized atomic arrangements that are not found in any of the crystalline states.

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Nature 589, 22-23 (2021)

doi: https://doi.org/10.1038/d41586-020-03574-w

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