Signatures of paracrystallinity in amorphous silicon

TL;DR

This paper demonstrates that signatures of paracrystallinity in amorphous silicon are consistent with experimental data, using advanced simulations to clarify the local order within the disordered network.

Contribution

The study provides the first systematic simulation-based evidence that paracrystalline structures can explain experimental observations in amorphous silicon.

Findings

Paracrystalline signatures are compatible with experimental data.

Machine-learning simulations elucidate the boundary between amorphous and crystalline states.

Structural and energy descriptors support the paracrystalline model.

Abstract

The structure of amorphous silicon (a-Si) has been studied for decades. The two main theories are based on a continuous random network and on a `paracrystalline' model, respectively -- the latter being defined as showing localized structural order resembling the crystalline state whilst retaining an overall amorphous network. However, the extent of this local order has been unclear, and experimental data have led to conflicting interpretations. Here we show that signatures of paracrystallinity in an otherwise disordered network are indeed compatible with the existing body of experimental observations for a-Si. We use quantum-mechanically accurate, machine-learning-driven simulations to systematically sample the configurational space of quenched a-Si, thereby allowing us to elucidate the boundary between amorphization and crystallization. We analyze our dataset using structural and local-energy descriptors to show that paracrystalline models are consistent with experiments in both regards. Our work provides a unified explanation for seemingly conflicting theories in one of the most widely studied amorphous networks.