Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon

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‐based techniques can give new, quantitative chemical insight into the atomic‐scale stru...

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Published inAngewandte Chemie International Edition Vol. 58; no. 21; pp. 7057 - 7061
Main Authors Bernstein, Noam, Bhattarai, Bishal, Csányi, Gábor, Drabold, David A., Elliott, Stephen R., Deringer, Volker L.
Format Journal Article
LanguageEnglish
Published Germany Wiley Subscription Services, Inc 20.05.2019
John Wiley and Sons Inc
EditionInternational ed. in English
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Summary: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‐based techniques can give new, quantitative chemical insight into the atomic‐scale structure of amorphous silicon (a‐Si). We combine a quantitative description of the nearest‐ and next‐nearest‐neighbor structure with a quantitative description of local stability. The analysis is applied to an ensemble of a‐Si networks in which we tailor the degree of ordering by varying the quench rates down to 1010 K s−1. Our approach associates coordination defects in a‐Si with distinct 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 therefore expected to have more general significance for developing a quantitative understanding of liquid and amorphous states of matter. In silico(n): Machine learning makes it possible to quantify the local structure in amorphous solids and the local atomically resolved energy at the same time, as demonstrated here for an ensemble of amorphous and liquid Si structures.
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ISSN:1433-7851
1521-3773
DOI:10.1002/anie.201902625