CUED Publications database

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

Bernstein, N and Bhattarai, B and Csányi, G and Drabold, DA and Elliott, SR and Deringer, VL (2019) Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon. Angew Chem Int Ed Engl, 58. pp. 7057-7061. ISSN 1521-3773

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: amorphous materials computational chemistry continuous random networks machine learning silicon
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Div C > Materials Engineering
Depositing User: Cron Job
Date Deposited: 07 Mar 2019 03:42
Last Modified: 20 Jun 2019 02:49
DOI: 10.1002/anie.201902625