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 amorph

SUBMITTER: Rosset LAM 

PROVIDER: S-EPMC11894222 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature