Deep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials.

ABSTRACT: Atomic dopants and defects play a crucial role in creating new functionalities in 2D transition metal dichalcogenides (2D TMDs). Therefore, atomic-scale identification and their quantification warrant precise engineering that widens their application to many fields, ranging from development of optoelectronic devices to magnetic semiconductors. Scanning transmission electron microscopy with a sub-Å probe has provided a facile way to observe local dopants and defects in 2D TMDs. However, manual data analytics of experimental images is a time-consuming task, and often requires subjective decisions to interpret observed signals. Therefore, an approach is required to automate the detection and classification of dopants and defects. In this study, based on a deep learning algorithm, fully convol

SUBMITTER: Yang SH 

PROVIDER: S-EPMC8373156 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature