ArticleScience advances2019
Building and exploring libraries of atomic defects in graphene: Scanning transmission electron and scanning tunneling microscopy study.
Article in Science advances, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Straintronics and Twistronics in 2D Materials: Structural Control and Predictive Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation.Accounts of chemical research · 2026Article
- afspm: A framework for manufacturer-agnostic automation in scanning probe microscopy.Beilstein journal of nanotechnology · 2026Article
- Real-space investigations of on-surface intermolecular radical transfer reactions assisted by persistent radicals.Science advances · 2025Article
- Automated image acquisition and analysis of graphene and hexagonal boron nitride from pristine to highly defective and amorphous structures.Scientific reports · 2024Article
- Localization and segmentation of atomic columns in supported nanoparticles for fast scanning transmission electron microscopy.npj computational materials · 2024Article
- Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials.iScience · 2023Review
- Learning motifs and their hierarchies in atomic resolution microscopy.Science advances · 2022Article
- QUAM-AFM: A Free Database for Molecular Identification by Atomic Force Microscopy.Journal of chemical information and modeling · 2022Article
- Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning.Nature communications · 2021Article
- Exploring order parameters and dynamic processes in disordered systems via variational autoencoders.Science advances · 2021Article
- The role of convolutional neural networks in scanning probe microscopy: a review.Beilstein journal of nanotechnology · 2021Review
- Machine Learning to Reveal Nanoparticle Dynamics from Liquid-Phase TEM Videos.ACS central science · 2020Article
- Machine learning and big scientific data.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2020Article
- Materials Science in the AI age: high-throughput library generation, machine learning and a pathway from correlations to the underpinning physics.MRS communications · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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