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

Deringer, Volker L.; Bernstein, Noam; Bartók, Albert P.; Cliffe, Matthew J.; Kerber, Rachel N.; Marbella, Lauren E.; Grey, Clare P.; Elliott, Stephen R.; Csányi, Gábor

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.

Citation

Deringer, V. L., Bernstein, N., Bartók, A. P., Cliffe, M. J., Kerber, R. N., Marbella, L. E., Grey, C. P., Elliott, S. R., & Csányi, G. (2018). Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics. Journal of Physical Chemistry Letters, 9(11), 2879-2885. https://doi.org/10.1021/acs.jpclett.8b00902

• 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
• Publisher: American Chemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 11
• Pages: 2879-2885
• DOI: https://doi.org/10.1021/acs.jpclett.8b00902
• Keywords: General Materials Science
• Public URL: https://nottingham-repository.worktribe.com/output/1430902
• Publisher URL: https://pubs.acs.org/doi/10.1021/acs.jpclett.8b00902
• Contract Date: Dec 20, 2018