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Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Bernstein, Noam 
Bartók, Albert P 
Cliffe, Matthew J 
Kerber, Rachel N 

Abstract

Amorphous silicon ( a-Si) is a widely studied noncrystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 1011 K/s (that is, on the 10 ns time scale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4096-atom system that correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials.

Description

Keywords

cond-mat.mtrl-sci, cond-mat.mtrl-sci

Journal Title

J Phys Chem Lett

Conference Name

Journal ISSN

1948-7185
1948-7185

Volume Title

9

Publisher

American Chemical Society (ACS)
Sponsorship
Isaac Newton Trust (1624(n))
Isaac Newton Trust (17.08(c))
Leverhulme Trust (ECF-2017-278)
Engineering and Physical Sciences Research Council (EP/P022596/1)