Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks.

Recent advances in machine learning (ML) have highlighted a novel challenge concerning the quality and quantity of data required to effectively train algorithms in supervised ML procedures. This article introduces a data augmentation (DA) strategy for electron energy loss spectroscopy (EELS) data, employing generative adversarial networks (GANs). We present an innovative approach, called the data augmentation generative adversarial network (DAG), which facilitates data generation from a very limited number of spectra, around 100.

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.

Direct Evidence of a Graded Magnetic Interface in Bimagnetic Core/Shell Nanoparticles Using Electron Magnetic Circular Dichroism (EMCD).

Interfaces play a crucial role in composite magnetic materials and particularly in bimagnetic core/shell nanoparticles. However, resolving the microscopic magnetic structure of these nanoparticles is rather complex. Here, we investigate the local magnetization of antiferromagnetic/ferrimagnetic FeO/FeO core/shell nanocubes by electron magnetic circular dichroism (EMCD).

Support vector machine for EELS oxidation state determination.

Electron Energy-Loss Spectroscopy (EELS) is a powerful and versatile spectroscopic technique used to study the composition and local optoelectronic properties of nanometric materials. Currently, this technique is generating large amounts of spectra per experiment, producing a huge quantity of data to analyse. Several strategies can be applied in order to classify these data to map physical properties at the nanoscale.

Precise Size Control of the Growth of FeO Nanocubes over a Wide Size Range Using a Rationally Designed One-Pot Synthesis.

The physicochemical properties of spinel oxide magnetic nanoparticles depend critically on both their size and shape. In particular, spinel oxide nanocrystals with cubic morphology have shown superior properties in comparison to their spherical counterparts in a variety of fields, like, for example, biomedicine. Therefore, having an accurate control over the nanoparticle shape and size, while preserving the crystallinity, becomes crucial for many applications.