CUED Publications database

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

Deringer, VL and Bernstein, N and Bartók, AP and Cliffe, MJ and Kerber, RN and Marbella, LE and Grey, CP and Elliott, SR and Csanyi, G (2018) Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics. Journal of Physical Chemistry Letters, 9. pp. 2879-2885. (Unpublished)

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Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural mod-els 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 10^11 K/s (that is, on the 10 ns timescale), 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 4,096-atom system which 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.

Item Type: Article
Uncontrolled Keywords: cond-mat.mtrl-sci cond-mat.mtrl-sci
Divisions: Div C > Applied Mechanics
Div C > Materials Engineering
Depositing User: Cron Job
Date Deposited: 21 Mar 2018 20:06
Last Modified: 02 Mar 2021 07:41
DOI: 10.1021/acs.jpclett.8b00902