Electron microscopy studies of soft nanomaterials

Z Lyu, L Yao, W Chen, FC Kalutantirige… - Chemical …, 2023 - ACS Publications
This review highlights recent efforts on applying electron microscopy (EM) to soft (including
biological) nanomaterials. We will show how developments of both the hardware and …

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 …

Machine learning in nuclear materials research

D Morgan, G Pilania, A Couet, BP Uberuaga… - Current Opinion in Solid …, 2022 - Elsevier
Nuclear materials are often demanded to function for extended time in extreme
environments, including high radiation fluxes with associated transmutations, high …

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 …

Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs

R Jacobs, M Shen, Y Liu, W Hao, X Li, R He… - Cell Reports Physical …, 2022 - cell.com
Transmission electron microscopy (TEM) is a popular method for characterizing and
quantifying defects in materials. Analyzing digitized TEM images is typically done manually …

Mineral detection of neutrinos and dark matter. A whitepaper

S Baum, P Stengel, N Abe, JF Acevedo… - Physics of the Dark …, 2023 - Elsevier
Minerals are solid state nuclear track detectors—nuclear recoils in a mineral leave latent
damage to the crystal structure. Depending on the mineral and its temperature, the damage …

A survey of real-time surface defect inspection methods based on deep learning

Y Liu, C Zhang, X Dong - Artificial Intelligence Review, 2023 - Springer
In recent years, deep learning methods have been widely used in various industrial
scenarios, promoting industrial intelligence. Real-time surface defect inspection of industrial …

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

R Jacobs, LE Schultz, A Scourtas… - Machine Learning …, 2024 - iopscience.iop.org
One compelling vision of the future of materials discovery and design involves the use of
machine learning (ML) models to predict materials properties and then rapidly find materials …

Deep learning of crystalline defects from TEM images: a solution for the problem of 'never enough training data'

K Govind, D Oliveros, A Dlouhy… - Machine Learning …, 2024 - iopscience.iop.org
Crystalline defects, such as line-like dislocations, play an important role for the performance
and reliability of many metallic devices. Their interaction and evolution still poses a multitude …

Materials swelling revealed through automated semantic segmentation of cavities in electron microscopy images

R Jacobs, P Patki, MJ Lynch, S Chen, D Morgan… - Scientific reports, 2023 - nature.com
Accurately quantifying swelling of alloys that have undergone irradiation is essential for
understanding alloy performance in a nuclear reactor and critical for the safe and reliable …