Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics.
Bartók, Albert P
The journal of physical chemistry letters
American Chemical Society (ACS)
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Deringer, V., Bernstein, N., Bartók, A. P., Cliffe, M., Kerber, R., Marbella, L., Grey, C., et al. (2018). Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics.. The journal of physical chemistry letters, 9 (11), 2879-2885. https://doi.org/10.1021/acs.jpclett.8b00902
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.
Isaac Newton Trust (1624(n))
Isaac Newton Trust (17.08(c))
Leverhulme Trust (ECF-2017-278)
External DOI: https://doi.org/10.1021/acs.jpclett.8b00902
This record's URL: https://www.repository.cam.ac.uk/handle/1810/280679
Attribution 4.0 International
Licence URL: https://creativecommons.org/licenses/by/4.0/
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