Understanding defects in amorphous silicon with million-atom simulations and machine learning

TL;DR

This study uses large-scale atomistic simulations combined with machine learning to classify and understand the complex defect structures in amorphous silicon, challenging existing models and revealing new defect behaviors.

Contribution

It introduces a universal defect classification in amorphous silicon using machine learning on a million-atom model, providing new insights into defect environments and clustering.

Findings

Fivefold-coordinated atoms have diverse local environments.

Fivefold defects tend to cluster, unlike threefold defects.

The study suggests revising the floating-bond defect model.

Abstract

The structure of amorphous silicon is widely thought of as a fourfold-connected random network, and yet it is defective atoms, with fewer or more than four bonds, that make it particularly interesting. Despite many attempts to explain such "dangling-bond" and "floating-bond" defects, respectively, a unified understanding is still missing. Here, we show that atomistic machine-learning methods can reveal the complex structural and energetic landscape of defects in amorphous silicon. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and more than ten thousand defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned local atomic energies to develop a universal classification of the different types of defects in amorphous silicon. The results suggest a revision of the established floating-bond model by showing that fivefold-coordinated atoms in amorphous silicon exhibit a wide range of local environments, and it is shown that fivefold (but not threefold) coordination defects tend to cluster together. Our study provides new insights into one of the most widely studied amorphous solids, and has general implications for modelling and understanding defects in disordered materials beyond silicon alone.