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Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics


Volker L. Deringer

Noam Bernstein

Albert P.

Rachel N. Kerber

Lauren E. Marbella

Clare P. Grey

Stephen R. Elliott


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.


Deringer, V. L., Bernstein, N., Bartók, A. P., Cliffe, M. J., Kerber, R. N., Marbella, L. E., …Csányi, G. (2018). Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics. Journal of Physical Chemistry Letters, 9(11), 2879-2885.

Journal Article Type Article
Acceptance Date May 12, 2018
Online Publication Date May 12, 2018
Publication Date Jun 7, 2018
Deposit Date Dec 20, 2018
Publicly Available Date Dec 20, 2018
Journal The Journal of Physical Chemistry Letters
Print ISSN 1948-7185
Publisher American Chemical Society
Peer Reviewed Peer Reviewed
Volume 9
Issue 11
Pages 2879-2885
Keywords General Materials Science
Public URL
Publisher URL


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