Machine learning in nuclear materials research
Nuclear materials are often demanded to function for extended time in extreme
environments, including high radiation fluxes with associated transmutations, high …
environments, including high radiation fluxes with associated transmutations, high …
In situ transmission electron microscopy and artificial intelligence enabled data analytics for energy materials
Energy materials are vital to energy conversion and storage devices that make renewable
resources viable for electrification technologies. In situ transmission electron microscopy …
resources viable for electrification technologies. In situ transmission electron microscopy …
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 …
materials science, with rapid progress made in just the past two years. Scanning and …
Materials informatics for mechanical deformation: A review of applications and challenges
K Frydrych, K Karimi, M Pecelerowicz, R Alvarez… - Materials, 2021 - mdpi.com
In the design and development of novel materials that have excellent mechanical properties,
classification and regression methods have been diversely used across mechanical …
classification and regression methods have been diversely used across mechanical …
Performance and limitations of deep learning semantic segmentation of multiple defects in transmission electron micrographs
Transmission electron microscopy (TEM) is a popular method for characterizing and
quantifying defects in materials. Analyzing digitized TEM images is typically done manually …
quantifying defects in materials. Analyzing digitized TEM images is typically done manually …
DefectTrack: a deep learning-based multi-object tracking algorithm for quantitative defect analysis of in-situ TEM videos in real-time
In-situ irradiation transmission electron microscopy (TEM) offers unique insights into the
millisecond-timescale post-cascade process, such as the lifetime and thermal stability of …
millisecond-timescale post-cascade process, such as the lifetime and thermal stability of …
[HTML][HTML] Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks
Abstract Machine Learning (ML) strategies applied to Scanning and conventional
Transmission Electron Microscopy have become a valuable tool for analyzing the large …
Transmission Electron Microscopy have become a valuable tool for analyzing the large …
In-situ TEM investigation of void swelling in nickel under irradiation with analysis aided by computer vision
Understanding the stability of irradiation-induced voids in materials is important for
engineering material's swelling behavior under irradiation. In-situ TEM offers a spatial and …
engineering material's swelling behavior under irradiation. In-situ TEM offers a spatial and …
Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility
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 …
machine learning (ML) models to predict materials properties and then rapidly find materials …
Materials swelling revealed through automated semantic segmentation of cavities in electron microscopy images
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 …
understanding alloy performance in a nuclear reactor and critical for the safe and reliable …