اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدMALTS: A tool to simulate Lorentz Transmission Electron Microscopy from micromagnetic simulations
216
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Here we describe the development of the MALTS software which is a generalised tool that simulates Lorentz Transmission Electron Microscopy (LTEM) contrast of thin magnetic nanostructures. Complex magnetic nanostructures typically have multiple stable domain structures. MALTS works in conjunction with the open access micromagnetic software Object Oriented Micromagnetic Framework or MuMax. Magnetically stable trial magnetisation states of the object of interest are input into MALTS and simulated LTEM images are output. MALTS computes the magnetic and electric phases accrued by the transmitted electrons via the Aharonov-Bohm expressions. Transfer and envelope functions are used to simulate the progression of the electron wave through the microscope lenses. The final contrast image due to these effects is determined by Fourier Optics. Similar approaches have been used previously for simulations of specific cases of LTEM contrast. The novelty here is the integration with micromagnetic codes via a simple user interface enabling the computation of the contrast from any structure. The output from MALTS is in good agreement with both experimental data and published LTEM simulations. A widely-available generalized code for the analysis of Lorentz contrast addresses is a much needed step towards the use of LTEM as a standardized laboratory technique.
قيم البحث
اقرأ أيضاً
This chapter introduces how to run molecular dynamics simulations for DNA origami using the oxDNA coarse-grained model.
A micromagnetic study of epitaxial micron-sized iron dots is reported through the analysis of Fresnel contrast in Lorentz Microscopy. Their use is reviewed and developed through analysis of various magnetic structures in such dots. Simple Landau conf
iguration is used to investigate various aspects of asymmetric Bloch domain walls. The experimental width of such a complex wall is first derived and its value is discussed with the help of micromagnetic simulations. Combination of these two approaches enables us to define what is really extracted when estimating asymmetric wall width in Lorentz Microscopy. Moreover, quantitative data on the magnetization inside the dot is retrieved using phase retrieval as well as new informations on the degrees of freedom of such walls. Finally, it is shown how the existence and the propagation of a surface vortex can be characterized and monitored. This demonstrates the ability to reach a magnetic sensitivity a priori hidden in Fresnel contrast, based on an original image treatment and backed-up by the evaluation of contrasts obtained from micromagnetic simulations.
Scanning transmission electron microscopy (STEM) is now the primary tool for exploring functional materials on the atomic level. Often, features of interest are highly localized in specific regions in the material, such as ferroelectric domain walls,
extended defects, or second phase inclusions. Selecting regions to image for structural and chemical discovery via atomically resolved imaging has traditionally proceeded via human operators making semi-informed judgements on sampling locations and parameters. Recent efforts at automation for structural and physical discovery have pointed towards the use of active learning methods that utilize Bayesian optimization with surrogate models to quickly find relevant regions of interest. Yet despite the potential importance of this direction, there is a general lack of certainty in selecting relevant control algorithms and how to balance a priori knowledge of the material system with knowledge derived during experimentation. Here we address this gap by developing the automated experiment workflows with several combinations to both illustrate the effects of these choices and demonstrate the tradeoffs associated with each in terms of accuracy, robustness, and susceptibility to hyperparameters for structural discovery. We discuss possible methods to build descriptors using the raw image data and deep learning based semantic segmentation, as well as the implementation of variational autoencoder based representation. Furthermore, each workflow is applied to a range of feature sizes including NiO pillars within a La:SrMnO$_3$ matrix, ferroelectric domains in BiFeO$_3$, and topological defects in graphene. The code developed in this manuscript are open sourced and will be released at github.com/creangnc/AE_Workflows.
Increasing interest in three-dimensional nanostructures adds impetus to electron microscopy techniques capable of imaging at or below the nanoscale in three dimensions. We present a reconstruction algorithm that takes as input a focal series of four-
dimensional scanning transmission electron microscopy (4D-STEM) data and transcends the prevalent structure retrieval algorithm assumption of a very thin specimen homogenous along the optic axis. We demonstrate this approach by reconstructing the different layers of a lead iridate (Pb$_2$Ir$_2$O$_7$) and yttrium-stabilized zirconia (Y$_{0.095}$Zr$_{0.905}$O$_2$) heterostructure from data acquired with the specimen in a single plan-view orientation, with the epitaxial layers stacked along the beam direction.
Recording atomic-resolution transmission electron microscopy (TEM) images is becoming increasingly routine. A new bottleneck is then analyzing this information, which often involves time-consuming manual structural identification. We have developed a
deep learning-based algorithm for recognition of the local structure in TEM images, which is stable to microscope parameters and noise. The neural network is trained entirely from simulation but is capable of making reliable predictions on experimental images. We apply the method to single sheets of defected graphene, and to metallic nanoparticles on an oxide support.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
