Signatures of paracrystallinity in amorphous silicon from machine-learning-driven molecular dynamics

Abstract

The structure of amorphous silicon has been studied for decades. The two main theories are based on a continuous random network and on a ‘paracrystalline’ model, respectively—the latter 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 experimental observations for amorphous silicon. We use quantum-mechanically accurate, machine-learning-driven simulations to systematically sample the configurational space of quenched silicon, 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.