[HTML][HTML] Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
Nanomaterials: Classification, properties, and environmental toxicities
TA Saleh - Environmental Technology & Innovation, 2020 - Elsevier
Nanomaterials (NMs) are gaining significance in technological applications due to their
tunable chemical, physical, and mechanical properties and enhanced performance when …
tunable chemical, physical, and mechanical properties and enhanced performance when …
[HTML][HTML] Automatic quantification and classification of microplastics in scanning electron micrographs via deep learning
B Shi, M Patel, D Yu, J Yan, Z Li, D Petriw… - Science of The Total …, 2022 - Elsevier
Microplastics quantification and classification are demanding jobs to monitor microplastic
pollution and evaluate the potential health risks. In this paper, microplastics from daily …
pollution and evaluate the potential health risks. In this paper, microplastics from daily …
[HTML][HTML] Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook
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 …
aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine …
[HTML][HTML] Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
[HTML][HTML] Microstructure segmentation with deep learning encoders pre-trained on a large microscopy dataset
This study examined the improvement of microscopy segmentation intersection over union
accuracy by transfer learning from a large dataset of microscopy images called MicroNet …
accuracy by transfer learning from a large dataset of microscopy images called MicroNet …
[HTML][HTML] A deep learning approach for complex microstructure inference
Automated, reliable, and objective microstructure inference from micrographs is essential for
a comprehensive understanding of process-microstructure-property relations and tailored …
a comprehensive understanding of process-microstructure-property relations and tailored …
[HTML][HTML] Using Machine Learning to make nanomaterials sustainable
JJ Scott-Fordsmand, MJB Amorim - Science of The Total Environment, 2023 - Elsevier
Sustainable development is a key challenge for contemporary human societies; failure to
achieve sustainability could threaten human survival. In this review article, we illustrate how …
achieve sustainability could threaten human survival. In this review article, we illustrate how …
Image-based machine learning for materials science
L Zhang, S Shao - Journal of Applied Physics, 2022 - pubs.aip.org
Materials research studies are dealing with a large number of images, which can now be
facilitated via image-based machine learning techniques. In this article, we review recent …
facilitated via image-based machine learning techniques. In this article, we review recent …
Expanding the horizons of machine learning in nanomaterials to chiral nanostructures
V Kuznetsova, Á Coogan, D Botov… - Advanced …, 2024 - Wiley Online Library
Abstract Machine learning holds significant research potential in the field of nanotechnology,
enabling nanomaterial structure and property predictions, facilitating materials design and …
enabling nanomaterial structure and property predictions, facilitating materials design and …