Defect engineering of two-dimensional transition-metal dichalcogenides: applications, challenges, and opportunities

Q Liang, Q Zhang, X Zhao, M Liu, ATS Wee - ACS nano, 2021 - ACS Publications
Atomic defects, being the most prevalent zero-dimensional topological defects, are
ubiquitous in a wide range of 2D transition-metal dichalcogenides (TMDs). They could be …

Single-atom engineering to ignite 2D transition metal dichalcogenide based catalysis: Fundamentals, progress, and beyond

X Wang, Y Zhang, J Wu, Z Zhang, Q Liao… - Chemical …, 2021 - ACS Publications
Single-atom catalysis has been recognized as a pivotal milestone in the development
history of heterogeneous catalysis by virtue of its superior catalytic performance, ultrahigh …

Vacancy defects in 2D transition metal dichalcogenide electrocatalysts: From aggregated to atomic configuration

X Wang, J Wu, Y Zhang, Y Sun, K Ma, Y Xie… - Advanced …, 2023 - Wiley Online Library
Vacancy defect engineering has been well leveraged to flexibly shape comprehensive
physicochemical properties of diverse catalysts. In particular, growing research effort has …

Machine learning in scanning transmission electron microscopy

SV Kalinin, C Ophus, PM Voyles, R Erni… - Nature Reviews …, 2022 - nature.com
Scanning transmission electron microscopy (STEM) has emerged as a uniquely powerful
tool for structural and functional imaging of materials on the atomic level. Driven by …

Data‐Driven Materials Innovation and Applications

Z Wang, Z Sun, H Yin, X Liu, J Wang, H Zhao… - Advanced …, 2022 - Wiley Online Library
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

M Botifoll, I Pinto-Huguet, J Arbiol - Nanoscale Horizons, 2022 - pubs.rsc.org
In the last few years, electron microscopy has experienced a new methodological paradigm
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic …

R Lin, R Zhang, C Wang, XQ Yang, HL Xin - Scientific reports, 2021 - nature.com
Atom segmentation and localization, noise reduction and deblurring of atomic-resolution
scanning transmission electron microscopy (STEM) images with high precision and …

In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials

H Zheng, X Lu, K He - Journal of Energy Chemistry, 2022 - Elsevier
Energy materials are vital to energy conversion and storage devices that make renewable
resources viable for electrification technologies. In situ transmission electron microscopy …

Deep learning modeling in microscopy imaging: A review of materials science applications

M Ragone, R Shahabazian-Yassar, F Mashayek… - Progress in Materials …, 2023 - Elsevier
The accurate analysis of microscopy images representing various materials obtained in
scanning probe microscopy, scanning tunneling microscopy, and transmission electron …

Deep learning object detection in materials science: Current state and future directions

R Jacobs - Computational Materials Science, 2022 - Elsevier
Deep learning-based object detection models have recently found widespread use in
materials science, with rapid progress made in just the past two years. Scanning and …