Accession Number:

AD1096665

Title:

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

Descriptive Note:

Journal Article - Open Access

Corporate Author:

NAVAL RESEARCH LAB WASHINGTON DC WASHINGTON United States

Report Date:

2019-04-17

Pagination or Media Count:

5.0

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 1010Ks-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.

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE