Building and exploring libraries of atomic defects in graphene: scanning transmission electron and scanning tunneling microscopy study

TL;DR

This study develops a method to create and analyze libraries of atomic defects in graphene using electron and scanning tunneling microscopy, combined with deep learning and density functional theory, enabling detailed defect characterization.

Contribution

The paper introduces an automated approach to generate and identify defect libraries in graphene, integrating microscopy, deep learning, and theoretical modeling for comprehensive defect analysis.

Findings

Created defect libraries with electron beam techniques

Automated defect recognition using deep learning

Correlated STM signatures with defect types

Abstract

Population and distribution of defects is one of the primary parameters controlling materials functionality, are often non-ergodic and strongly dependent on synthesis history, and are rarely amenable to direct theoretical prediction. Here, dynamic electron beam-induced transformations in Si deposited on a graphene monolayer are used to create libraries of the possible Si and carbon vacancy defects. Automated image analysis and recognition based on deep learning networks is developed to identify and enumerate the defects, creating a library of (meta) stable defect configurations. The electronic properties of the sample surface are further explored by atomically resolved scanning tunneling microscopy (STM). Density functional theory is used to estimate the STM signatures of the classified defects from the created library, allowing for the identification of several defect types across the imaging platforms. This approach allows automatic creation of defect libraries in solids, exploring the metastable configurations always present in real materials, and correlative studies with other atomically-resolved techniques, providing comprehensive insight into defect functionalities. Such libraries will be of critical importance in automated AI-assisted workflows for materials prediction and atom-by atom manipulation via electron beams and scanning probes.