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 …

Machine learning for nanoplasmonics

JF Masson, JS Biggins, E Ringe - Nature Nanotechnology, 2023 - nature.com
Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling
the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical …

Atomically dispersed iron sites with a nitrogen–carbon coating as highly active and durable oxygen reduction catalysts for fuel cells

S Liu, C Li, MJ Zachman, Y Zeng, H Yu, B Li, M Wang… - Nature Energy, 2022 - nature.com
Nitrogen-coordinated single atom iron sites (FeN4) embedded in carbon (Fe–N–C) are the
most active platinum group metal-free oxygen reduction catalysts for proton-exchange …

AtomAI framework for deep learning analysis of image and spectroscopy data in electron and scanning probe microscopy

M Ziatdinov, A Ghosh, CY Wong… - Nature Machine …, 2022 - nature.com
Over the past several decades, electron and scanning probe microscopes have become
critical components of condensed matter physics, materials science and chemistry research …

Experimental discovery of structure–property relationships in ferroelectric materials via active learning

Y Liu, KP Kelley, RK Vasudevan, H Funakubo… - Nature Machine …, 2022 - nature.com
Emergent functionalities of structural and topological defects in ferroelectric materials
underpin an extremely broad spectrum of applications ranging from domain wall electronics …

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 …

Interface aspects in all‐solid‐state Li‐based batteries reviewed

C Chen, M Jiang, T Zhou, L Raijmakers… - Advanced Energy …, 2021 - Wiley Online Library
Extensive efforts have been made to improve the Li‐ionic conductivity of solid electrolytes
(SE) for developing promising all‐solid‐state Li‐based batteries (ASSB). Recent studies …

[图书][B] Electron energy-loss spectroscopy in the electron microscope

RF Egerton - 2011 - books.google.com
Within the last 30 years, electron energy-loss spectroscopy (EELS) has become a standard
analytical technique used in the transmission electron microscope to extract chemical and …

Electron energy-loss spectroscopy in the TEM

RF Egerton - Reports on Progress in Physics, 2008 - iopscience.iop.org
Electron energy-loss spectroscopy (EELS) is an analytical technique that measures the
change in kinetic energy of electrons after they have interacted with a specimen. When …

Automated and autonomous experiments in electron and scanning probe microscopy

SV Kalinin, M Ziatdinov, J Hinkle, S Jesse, A Ghosh… - ACS …, 2021 - ACS Publications
Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable
part of physics research, with domain applications ranging from theory and materials …