Machine learning reveals the complexity of dense amorphous silicon

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. Silicon is one of a small class of elements whose density increases on melting2. This unusual behaviour is shared with crystalline ice, which floats on top of liquid water.