Basics and applications of ELNES calculations

Simulated carbon K edge spectral database of organic molecules

Here we provide a database of simulated carbon K (C-K) edge core loss spectra of 117,340 symmetrically unique sites in 22,155 molecules with no more than eight non-hydrogen atoms (C, O, N, and F). Our database contains C-K edge spectra of each carbon site and those of molecules along with their excitation energies. Our database is useful for analyzing experimental spectrum and conducting spectrum informatics on organic materials.

Measurement(s)	electron energy loss spectroscopy
Technology Type(s)	density functional theory calculation
Factor Type(s)	organic molecules • carbon sites in a organic molecule

Automatic determination of the spectrum-structure relationship by tree structure-based unsupervised and supervised learning

Spectroscopy is widely used for the analysis of chemical, vibrational, and bonding information. Interpretations of the spectral features have been performed by comparing the objective spectra with reference spectra from experiments or simulations. However, the interpretation process by humans is not always straightforward, especially for spectra obtained from unknown or new materials. In the present study, we developed a method using machine learning techniques to obtain human-like interpretation automatically. We combined unsupervised and supervised learning methods; then applied it to the spectrum database which includes more than 400 spectra of water and organic molecules containing various ligands and chemical bonds. The proposed method has successfully found the correlations between the spectral features and descriptors of the atoms, bonds, and ligands. We demonstrated that the proposed method enabled the automatic determination of reasonable spectrum-structure relationships such as between π* resonance in C-K edges and multiple bonds. The proposed method enables the automatic determination of physically and chemically reasonable spectrum-structure relationships without arbitrariness in data-driven manner, which is considerably difficult only with simulation or conventional machine leaning techniques. Such relationships are useful for understanding what structural parameters cause changes in the spectrum, providing a way for the better interpretation of spatial distributed or time evolutionary data. Furthermore, although the present work focused on the ELNES/XANES spectrum from small organic molecules, the proposed method can be readily extended to other spectral data. It is expected to contribute to a better understanding of the spectrum-structure relationship in various spectroscopy applications.

Revealing Spatial Distribution of Al-Coordinated Species in a Phase-Separated Aluminosilicate Glass by STEM-EELS

Structure determination of glass remains an important issue in glass science. The electron microscope with its high spatial resolution and versatile functions has been widely applied to observe phase separation and structural heterogeneity in glass. While elemental analysis such as energy dispersive spectroscopy (EDS) and electron energy loss spectroscopy (EELS) may provide local compositional information with nanometer-scale resolution, structural information in a glass network cannot be directly obtained. Here, a novel way to probe local coordination is employed using electron energy loss fine structure (ELNES) in the scanning transmission electron microscope (STEM). The method is demonstrated in a phase-separated aluminosilicate glass with multiple Al-coordinated species. With the support of ab initio calculation, two exciton-like peaks in the Al L_2,3-edge at around 77 and 80 eV are attributed to 4-fold and 5,6-fold Al excitations, respectively. Mapping of the relative intensity ratio for two peaks in a phase-separated microstructure reveals a heterogeneous distribution of highly coordinated Al species in real space. The finding is in agreement with previous MD simulation that 5- and 6-fold Al species are favored to form in the Al-rich phase. This work has demonstrated that complex network structure within the phase-separated region can now be studied via STEM-EELS.

Core hole screened electron energy loss calculations of beam damaged lithium fluoride

A method of calculating the magnitude of the core hole screening of lithium materials is implemented for the simulation of Energy Loss Near Edge Structure (ELNES). ELNES is calculated for a range of lithium materials resulting in improved agreement between calculation and experiment. The technique uses linear response theory to relate the electron density to the core hole shielding contribution from the valence electrons in a crystal. This contribution is then implemented via a non-integer core hole in final state rule calculations.

Quantitative Analysis Method for Nitrogen Electron Energy-Loss Near-Edge Structures in Nanocarbons Based on Density Functional Theory Calculations and Linear Regression

Nonmetallic heteroatoms found in carbon nanomaterials act as active sites and exhibit excellent catalytic performance. Owing to structural complexity and the limitations of characterization technology, the identification of active sites in nanocarbon is challenging and controversial. Electron energy-loss spectroscopy is an electron microscope technique with high spatial resolution and a powerful tool for identifying the arrangement of heteroatoms. However, structural information regarding the configuration and distribution of heteroatoms is difficult to obtain using existing analytical methods. Herein, we have developed a method for the quantitative analysis of electron energy-loss near-edge structures to identify accurately nitrogen species in nanocarbon. Based on this approach, the relative amounts of nitrogen species were obtained from linear regression with calculated spectra. The concentration distribution of nanocarbon obtained by this method was consistent with the result of X-ray photoelectron spectroscopy analysis at different depths. Therefore, this fitting method can be used for the quantitative analysis of nitrogen K-edge structures. This provides a new strategy for studying the structure-activity relationships of carbon-based materials and the further design of custom nanocarbon catalysts with high active site densities.

Machine learning approaches for ELNES/XANES

Materials characterization is indispensable for materials development. In particular, spectroscopy provides atomic configuration, chemical bonding and vibrational information, which are crucial for understanding the mechanism underlying the functions of a material. Despite its importance, the interpretation of spectra using human-driven methods, such as manual comparison of experimental spectra with reference/simulated spectra, is becoming difficult owing to the rapid increase in experimental spectral data. To overcome the limitations of such methods, we develop new data-driven approaches based on machine learning. Specifically, we use hierarchical clustering, a decision tree and a feedforward neural network to investigate the electron energy loss near edge structures (ELNES) spectrum, which is identical to the X-ray absorption near edge structure (XANES) spectrum. Hierarchical clustering and the decision tree are used to interpret and predict ELNES/XANES, while the feedforward neural network is used to obtain hidden information about the material structure and properties from the spectra. Further, we construct a prediction model that is robust against noise by data augmentation. Finally, we apply our method to noisy spectra and predict six properties accurately. In summary, the proposed approaches can pave the way for fast and accurate spectrum interpretation/prediction as well as local measurement of material functions.

Combining Pseudopotential and All Electron Density Functional Theory for the Efficient Calculation of Core Spectra Using a Multiresolution Approach

Broadly speaking, the calculation of core spectra such as electron energy loss spectra (EELS) at the level of density functional theory (DFT) usually relies on one of two approaches: conceptually more complex but computationally efficient projector augmented wave based approaches or more straightforward but computationally more intensive all electron (AE) based approaches. In this work we present an alternative method, which aims to find a middle ground between the two. Specifically, we have implemented an approach in the multiwavelet madness molecular DFT code that permits a combination of atoms treated at the AE and pseudopotential (PSP) level. Atoms for which one wishes to calculate the core edges are thus treated at an AE level, while the remainder can be treated at the PSP level. This is made possible thanks to the multiresolution approach of madness, which permits accurate and efficient calculations at both the AE and PSP level. Through examples of a small molecule and a carbon nanotube, we demonstrate the potential applications of our approach.

Distinguishing nitrogen-containing sites in SiO2/4H-SiC(0001) after nitric oxide annealing by X-ray absorption spectroscopy

The atomic structure of nitrogen at the SiO₂/4H-SiC(0001) interface has been investigated using X-ray absorption spectroscopy (XAS) in two nitric oxide annealed samples, one of which was oxidized in dry O₂ (NO-POA) prior to the experiment. The peak shapes and energies of the observed and simulated spectra are in agreement and indicate that the N-containing sites could be the substitutional C site at the interface for the NO-annealed sample and the interstitial site in the interior of SiC for the NO-POA-annealed sample. XAS analysis distinguished between the N-containing sites at the SiO₂/SiC interface.

Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy

Spectroscopy is indispensable for determining atomic configurations, chemical bondings, and vibrational behaviours, which are crucial information for materials development. Despite their importance, the interpretation of spectra using "human-driven" methods, such as the manual comparison of experimental spectra with reference/simulated spectra, is difficult due to the explosive increase in the number of experimental spectra to be observed. To overcome the limitations of the "human-driven" approach, we develop a new "data-driven" approach based on machine learning techniques by combining the layer clustering and decision tree methods. The proposed method is applied to the 46 oxygen-K edges of the ELNES/XANES spectra of oxide compounds. With this method, the spectra can be interpreted in accordance with the material information. Furthermore, we demonstrate that our method can predict spectral features from the material information. Our approach has the potential to provide information about a material that cannot be determined manually as well as predict a plausible spectrum from the geometric information alone.

Estimation of the molecular vibration of gases using electron microscopy

Reactions in gaseous phases and at gas/solid interfaces are widely used in industry. Understanding of the reaction mechanism, namely where, when, and how these gaseous reactions proceed, is crucial for the development of further efficient reaction systems. To achieve such an understanding, it is indispensable to grasp the dynamic behavior of the gaseous molecules at the active site of the chemical reaction. However, estimation of the dynamic behavior of gaseous molecules in specific nanometer-scale regions is always accompanied by great difficulties. Here, we propose a method for the identification of the dynamic behavior of gaseous molecules using an electron spectroscopy observed with a transmission electron microscope in combination with theoretical calculations. We found that our method can successfully identify the dynamic behavior of some gaseous molecules, such as O2 and CH4, and the sensitivity of the method is affected by the rigidity of the molecule. The method has potential to measure the local temperature of gaseous molecules as well. The knowledge obtained from this technique is fundamental for further high resolution studies of gaseous reactions using electron microscopy.