Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon.
Angewandte Chemie (International ed. in English)
John Wiley & Sons Ltd.
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Bernstein, N., Bhattarai, B., Csányi, G., Drabold, D. A., Elliott, S., & Deringer, V. (2019). Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon.. Angewandte Chemie (International ed. in English), 58 (21), 7057-7061. https://doi.org/10.1002/anie.201902625
Amorphous materials are being described by increasingly powerful computer simulations, but new approaches are still needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). We combine a quantitative description of nearest- and next-nearest-neighbor structure (through a similarity function or kernel) with a quantitative description of local stability (“machine-learned” atomic energies). We apply this analysis to an ensemble of a-Si net-works in which we tailor the degree of ordering by varying the quench rates down to 10^10 K/s (leading to a structural model that is lower in energy than the established bond-switching WWW network). Our approach associates coordination defects in a-Si with distinct energetic stability regions, and it has also been applied to liquid Si, where it traces a clear-cut transition in local energies during vitrification. The method is straightforward and inexpensive to apply, and it is therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter.
Isaac Newton Trust (17.08(c))
Leverhulme Trust (ECF-2017-278)
External DOI: https://doi.org/10.1002/anie.201902625
This record's URL: https://www.repository.cam.ac.uk/handle/1810/291938
Attribution 4.0 International
Licence URL: http://creativecommons.org/licenses/by/4.0/
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