Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon.
Angew Chem Int Ed Engl
; 58(21): 7057-7061, 2019 May 20.
Article
em En
| MEDLINE
| ID: mdl-30835962
Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We combine a quantitative description of the nearest- and next-nearest-neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a-Si networks in which we tailor the degree of ordering by varying the quench rates down to 1010 â
K s-1 . Our approach associates coordination defects in a-Si with distinct stability regions and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter.
Texto completo:
1
Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article