Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Apr 17, 2019
- Source ID
- 10.1002/ange.201902625
Entities
People
- Bishal Bhattarai
- David Drabold
- Gábor Csányi
- Noam Bernstein
- Stephen R Elliott
- Volker L. Deringer
Organizations
- Engineering and Physical Sciences Research Council
- Isaac Newton Trust
- Leverhulme Trust
- National Science Foundation
- Office of Naval Research
- Ohio University
- United States Naval Research Laboratory
- University of Cambridge