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 …

Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

近年来, 基于深度学习的表面缺陷检测技术广泛应用在各种工业场景中. 本文对近年来基于深度
学习的表面缺陷检测方法进行了梳理, 根据数据标签的不同将其分为全监督学习模型方法 …

Microscopic imaging providing the real-space information of matter, plays an important role
for understanding the correlations between structure and properties in the field of materials …

Research publications are the major repository of scientific knowledge. However, their
unstructured and highly heterogenous format creates a significant obstacle to large-scale …

Nuclear materials are often demanded to function for extended time in extreme
environments, including high radiation fluxes with associated transmutations, high …

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

As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

Automatic segmentation of key microstructural features in atomic-scale electron microscope
images is critical to improved understanding of structure–property relationships in many …

Energy materials are vital to energy conversion and storage devices that make renewable
resources viable for electrification technologies. In situ transmission electron microscopy …