Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics

TL;DR

This paper demonstrates that machine-learning-driven molecular dynamics can produce highly accurate atomistic models of amorphous silicon, matching experimental data and revealing detailed structural insights.

Contribution

The study introduces a machine-learning-based approach to generate realistic amorphous silicon structures with unprecedented accuracy and detail.

Findings

Achieved defect concentration below 2% in a-Si models.

Reproduced experimental diffraction data and NMR chemical shifts.

Closest agreement with experimental structure factor, including FSDP.

Abstract

Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained by harnessing the power of machine-learning algorithms to create interatomic potentials. Our best a-Si network is obtained by cooling from the melt in molecular-dynamics simulations, at a rate of 10$^{11}$ K/s (that is, on the 10 ns timescale). This structure shows a defect concentration of below 2% and agrees with experiments regarding excess energies, diffraction data, as well as $^{29}$Si solid-state NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4,096-atom system which correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning interatomic potentials for elucidating accurate structures and properties of amorphous functional materials.