Realistic inversion of diffraction data for an amorphous solid: the case of amorphous silicon

TL;DR

This paper introduces the FEAR method to accurately model amorphous silicon by fitting diffraction data and chemical information, resulting in a structure consistent with experiments and electronic properties.

Contribution

The study presents a novel FEAR approach that improves the accuracy and efficiency of modeling amorphous silicon from diffraction data compared to traditional methods.

Findings

FEAR model closely matches experimental diffraction data.

The model exhibits a small concentration of coordination defects.

Vibrational density of states and specific heat agree with experiments.

Abstract

We apply a new method "force enhanced atomic refinement" (FEAR) to create a computer model of amorphous silicon (a-Si), based upon the highly precise X-ray diffraction experiments of Laaziri et al. The logic underlying our calculation is to estimate the structure of a real sample a-Si using experimental data and chemical information included in a non-biased way, starting from random coordinates. The model is in close agreement with experiment and also sits at a suitable minimum energy according to density functional calculations. In agreement with experiments, we find a small concentration of coordination defects that we discuss, including their electronic consequences. The gap states in the FEAR model are delocalized compared to a continuous random network model. The method is more efficient and accurate, in the sense of fitting the diffraction data than conventional melt quench methods. We compute the vibrational density of states and the specific heat, and find that both compare favorably to experiments.