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 …

Nanomaterials (NMs) are gaining significance in technological applications due to their
tunable chemical, physical, and mechanical properties and enhanced performance when …

Microplastics quantification and classification are demanding jobs to monitor microplastic
pollution and evaluate the potential health risks. In this paper, microplastics from daily …

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 …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

This study examined the improvement of microscopy segmentation intersection over union
accuracy by transfer learning from a large dataset of microscopy images called MicroNet …

Automated, reliable, and objective microstructure inference from micrographs is essential for
a comprehensive understanding of process-microstructure-property relations and tailored …

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 …

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 …

Abstract Machine learning holds significant research potential in the field of nanotechnology,
enabling nanomaterial structure and property predictions, facilitating materials design and …