Segmentation of Static and Dynamic Atomic-Resolution Microscopy Data Sets with Unsupervised Machine Learning Using Local Symmetry Descriptors

We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages. Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of the Microscopy Society of America

Machine learning to empower electrohydrodynamic processing

Electrohydrodynamic (EHD) processes are promising healthcare fabrication technologies, as evidenced by the number of commercialised and food-and-drug administration (FDA)-approved products produced by these processes. Their ability to produce both rapidly and precisely nano-sized products provides them with a unique set of qualities that cannot be matched by other fabrication technologies. Consequently, this has stimulated the development of EHD processing to tackle other healthcare challenges. However, as with most technologies, time and resources will be needed to realise fully the potential EHD processes can offer. To address this bottleneck, researchers are adopting machine learning (ML), a subset of artificial intelligence, into their workflow. ML has already made ground-breaking advancements in the healthcare sector, and it is anticipated to do the same in the materials domain. Presently, the application of ML in fabrication technologies lags behind other sectors. To that end, this review showcases the progress made by ML for EHD workflows, demonstrating how the latter can benefit greatly from the former. In addition, we provide an introduction to the ML pipeline, to help encourage the use of ML for other EHD researchers. As discussed, the merger of ML with EHD has the potential to expedite novel discoveries and to automate the EHD workflow

Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering

Recently, there has been extensive use of artificial Intelligence (AI) in the field of material engineering. This can be attributed to the development of high performance computing and thereby feasibility to test deep learning models with large parameters. In this article we tried to review some of the latest developments in the applications of AI in material engineering.Comment: V

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Comparative of machine learning classification strategies for electron energy loss spectroscopy: Support vector machines and artificial neural networks

Machine Learning (ML) strategies applied to Scanning and conventional Transmission Electron Microscopy have become a valuable tool for analyzing the large volumes of data generated by various S/TEM techniques. In this work, we focus on Electron Energy Loss Spectroscopy (EELS) and study two ML techniques for classifying spectra in detail: Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Firstly, we systematically analyze the optimal configurations and architectures for ANN classifiers using random search and the tree-structured Parzen estimator methods. Secondly, a new kernel strategy is introduced for the soft-margin SVMs, the cosine kernel, which offers a significant advantage over the previously studied kernels and other ML classification strategies. This kernel allows us to bypass the normalization of EEL spectra, achieving accurate classification. This result is highly relevant for the EELS community since we also assess the impact of common normalization techniques on our spectra using Uniform Manifold Approximation and Projection (UMAP), revealing a strong bias introduced in the spectra once normalized. In order to evaluate and study both classification strategies, we focus on determining the oxidation state of transition metals through their EEL spectra, examining which feature is more suitable for oxidation state classification: the oxygen K peak or the transition metal white lines. Subsequently, we compare the resistance to energy loss shifts for both classifiers and present a strategy to improve their resistance. The results of this study suggest the use of soft-margin SVMs for simpler EELS classification tasks with a limited number of spectra, as they provide performance comparable to ANNs while requiring lower computational resources and reduced training times. Conversely, ANNs are better suited for handling complex classification problems with extensive training data

The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning

Machine learning (ML) has experienced rapid development in recent years and been widely applied to assist studies in various research areas. Two-dimensional (2D) materials, due to their unique chemical and physical properties, have been receiving increasing attention since the isolation of graphene. The combination of ML and 2D materials science has significantly accelerated the development of new functional 2D materials, and a timely review may inspire further ML-assisted 2D materials development. In this review, we provide a horizontal and vertical summary of the recent advances at the intersection of the fields of ML and 2D materials, discussing ML-assisted 2D materials preparation (design, discovery, and synthesis of 2D materials), atomistic structure analysis (structure identification and formation mechanism), and properties prediction (electronic properties, thermodynamic properties, mechanical properties, and other properties) and revealing their connections. Finally, we highlight current research challenges and provide insight into future research opportunities.This work was supported by the ANU Futures Scheme (Q4601024), the Australian Research Council (DP190100295, LE190100014), the National Natural Science Foundation of China (No. 51706114 and 51302166), Functional Materials Interfaces Genome (FIG) project, and Doctoral Fund of Ministry of Education of China (20133108120021)

Using Time-Resolved Electron Microscopy And Data Analytics To Quantify The Evolution Of Supported Metal Nanoparticles

Supported precious metal nanoparticles are important heterogeneous catalysts for both industrial processes and commercial products. Their high catalytic activity stems from their high surface free energy and under-coordinated surfaces, however these same properties destabilize the particles and cause them to grow and deactivate. While research studying the degradation of supported catalysts has been undertaken for decades, the exact mechanisms at play, and how the vary with reaction conditions, are not well understood. Advances in experimental instrumentation have positioned Transmission Electron Microscopy (TEM) as an ideal tool for characterizing the dynamic evolution of these nanoscale systems with both high spatial and temporal resolution. However, the difficulty of manually analyzing large in situ datasets to quantify nanostructural evolution remains a challenge. This dissertation focuses on combining in situ experimental observations with machine learning and data analytics to quantify image data and understand nanoparticle coarsening. The first thrust of this research is developing a machine-learning pipeline for automated image segmentation. By optimizing state-of-the-art deep learning segmentation models, we were able to rapidly segment and measure particles from thousands of TEM images in a reliable and reproducible fashion. Utilizing this automated image processing pipeline, we observed the evolution of a model catalyst at high temperature and assessed the competition between coarsening by evaporation and surface diffusion as a function of particle size and temperature. After developing a physical model to describe each mechanism, we were able to characterize particle interactions along the support and to identify a critical particle size which avoids degradation. Finally, we used a combination of temperature-dependent in situ experiments and Kinetic Monte Carlo simulations to understand how the rate of nanoparticle evaporation depends on nanoparticle morphology. Our mechanistic model allows us to understand how random structural fluctuations and surface roughening contribute to the evaporation process. In all, this research aims at developing techniques and data-rich quantitative methods for understanding how supported nanocatalysts can be engineered for optimal activity and lifetime