Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 9, Issue 11, Pages 2879-2885Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.8b00902
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Funding
- Alexander von Humboldt Foundation
- Isaac Newton Trust
- Office of Naval Research through the U.S. Naval Research Laboratory's basic research base program
- Collaborative Computational Project for NMR Crystallography
- EPSRC [EP/M022501/1, EP/L000202/1]
- Sidney Sussex College, University of Cambridge
- FP7Marie Curie International Incoming Fellowship
- EPSRC [EP/M022501/1, EP/P022596/1, EP/L000202/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/P022596/1] Funding Source: researchfish
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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 10(11)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 Si-29 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.
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