Understanding Defects in Amorphous Silicon with Million-Atom Simulations and Machine Learning.

ABSTRACT: The structure of amorphous silicon (a-Si) 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 use advanced computational chemistry methods to reveal the complex structural and energetic landscape of defects in a-Si. We study an ultra-large-scale, quantum-accurate structural model containing a million atoms, and thousands of individual defects, allowing reliable defect-related statistics to be obtained. We combine structural descriptors and machine-learned atomic energies to develop a classification of the different types of defects

SUBMITTER: Morrow JD 

PROVIDER: S-EPMC11497335 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature