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

Ziatdinov, Maxim; Dyck, Ondrej; Li, Xin; Sumpter, Bobby G.; Jesse, Stephen; Vasudevan, Rama K.; Kalinin, Sergei V.

doi:10.1126/sciadv.aaw8989

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

Journal Article · 26 September 2019 · Science Advances

DOI: https://doi.org/10.1126/sciadv.aaw8989 · OSTI ID:1607267

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

The presence and configurations of defects are primary components determining materials functionality. Their population and distribution are often nonergodic and dependent on synthesis history, and therefore 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 possible Si and carbon vacancy defects. Deep learning networks are developed for automated image analysis and recognition of the defects, creating a library of (meta) stable defect configurations. Density functional theory is used to estimate atomically resolved scanning tunneling microscopy (STM) signatures of the classified defects from the created library, allowing identification of several defect types across 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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1607267

Journal Information:: Science Advances, Journal Name: Science Advances Journal Issue: 9 Vol. 5; ISSN 2375-2548

Publisher:: AAASCopyright Statement

Country of Publication:: United States

Language:: English

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Robust multi-scale multi-feature deep learning for atomic and defect identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface Ziatdinov, Maxim; Fuchs, Udi; Owen, James H. G. arXiv https://doi.org/10.48550/arxiv.2002.04716	preprint	January 2020
Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk Ziatdinov, Maxim; Dyck, Ondrej; Jesse, Stephen Advanced Functional Materials, Vol. 29, Issue 52 https://doi.org/10.1002/adfm.201904480	journal	November 2019
Atomic mechanisms for the Si atom dynamics in graphene: chemical transformations at the edge and in the bulk Ziatdinov, Maxim; Dyck, Ondrej; Jesse, Stephen arXiv https://doi.org/10.48550/arxiv.1901.09322	text	January 2019

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