Unraveling the Stability of Layered Intercalation Compounds through First-Principles Calculations: Establishing a Linear Free Energy Relationship with Aqueous Ions.

Layered intercalation compounds, where atoms or molecules (intercalants) are inserted into layered materials (hosts), hold great potential for diverse applications. However, the lack of a systematic understanding of stable host-intercalant combinations poses challenges in materials design due to the vast combinatorial space. In this study, we performed first-principles calculations on 9024 compounds, unveiling a novel linear regression equation based on the principle of hard and soft acids and bases. This equation, incorporating the intercalant ion formation energy and ionic radius, quantitatively reveals the stability factors. Additionally, employing machine learning, we predicted regression coefficients from host properties, offering a comprehensive understanding and a predictive model for estimating the intercalation energy. Our work provides valuable insights into the energetics of layered intercalation compounds, facilitating targeted materials design.

Effect of inorganic material surface chemistry on structures and fracture behaviours of epoxy resin.

The mechanisms underlying the influence of the surface chemistry of inorganic materials on polymer structures and fracture behaviours near adhesive interfaces are not fully understood. This study demonstrates the first clear and direct evidence that molecular surface segregation and cross-linking of epoxy resin are driven by intermolecular forces at the inorganic surfaces alone, which can be linked directly to adhesive failure mechanisms. We prepare adhesive interfaces between epoxy resin and silicon substrates with varying surface chemistries (OH and H terminations) with a smoothness below 1 nm, which have different adhesive strengths by ~13 %. The epoxy resins within sub-nanometre distance from the surfaces with different chemistries exhibit distinct amine-to-epoxy ratios, cross-linked network structures, and adhesion energies. The OH- and H-terminated interfaces exhibit cohesive failure and interfacial delamination, respectively. The substrate surface chemistry impacts the cross-linked structures of the epoxy resins within several nanometres of the interfaces and the adsorption structures of molecules at the interfaces, which result in different fracture behaviours and adhesive strengths.

Microscopic chemical characterization of epoxy resin with scanning transmission electron microscopy - electron energy-loss spectroscopy.

The structural characterization of epoxy resins is essential to improve the understanding on their structure-property relationship for promising high-performance applications. Among all analytical techniques, scanning transmission electron microscopy-electron energy-loss spectroscopy (STEM-EELS) is a powerful tool for probing the chemical and structural information of various materials at a high spatial resolution. However, for sensitive materials, such as epoxy resins, the structural damage induced by electron-beam irradiation limits the spatial resolution in the STEM-EELS analysis. In this study, we demonstrated the extraction of the intrinsic features and structural characteristics of epoxy resins by STEM-EELS under electron doses below 1 e-/Å2 at room temperature. The reliability of the STEM-EELS analysis was confirmed by X-ray absorption spectroscopy and spectrum simulation as low- or non-damaged reference data. The investigation of the dependence of the epoxy resin on the electron dose and exposure time revealed the structural degradation associated with electron-beam irradiation, exploring the prospect of EELS for examining epoxy resin at low doses. Furthermore, the degradation mechanisms in the epoxy resin owing to electron-beam irradiation were revealed. These findings can promote the structural characterization of epoxy-resin-based composites and other soft materials.

Atomistic Probing of Defect-Engineered 2H-MoTe2 Monolayers.

Point defects dictate various physical, chemical, and optoelectronic properties of two-dimensional (2D) materials, and therefore, a rudimentary understanding of the formation and spatial distribution of point defects is a key to advancement in 2D material-based nanotechnology. In this work, we performed the demonstration to directly probe the point defects in 2H-MoTe2 monolayers that are tactically exposed to (i) 200 °C-vacuum-annealing and (ii) 532 nm-laser-illumination; and accordingly, we utilize a deep learning algorithm to classify and quantify the generated point defects. We discovered that tellurium-related defects are mainly generated in both 2H-MoTe2 samples; but interestingly, 200 °C-vacuum-annealing and 532 nm-laser-illumination modulate a strong n-type and strong p-type 2H-MoTe2, respectively. While 200 °C-vacuum-annealing generates tellurium vacancies or tellurium adatoms, 532 nm-laser-illumination prompts oxygen atoms to be adsorbed/chemisorbed at tellurium vacancies, giving rise to the p-type characteristic. This work significantly advances the current understanding of point defect engineering in 2H-MoTe2 monolayers and other 2D materials, which is critical for developing nanoscale devices with desired functionality.

Combinatorial characterization of metastable luminous silver cations.

Thermodynamically metastable glasses that can contain metastable species are important functional materials. X-ray absorption near-edge structure (XANES) spectroscopy is an effective technique for determining the valence states of cations, especially for the doping element in phosphors. Herein, we first confirm the valence change of silver cations from monovalent to trivalent in aluminophosphate glasses by X-ray irradiation using a combination of Ag L3-edge XANES, electron spin resonance, and simulated XANES spectra based on first-principles calculations. The absorption edge of the experimental and simulated XANES spectra demonstrate the spectral features of Ag(III), confirming that AgO exists as Ag(I)Ag(III)O2. A part of Ag(I) changes to Ag(III) by X-ray irradiation, and the generation of Ag(III) is saturated after high irradiation doses, in good agreement with conventional radiophotoluminescence (RPL) behaviour. The structural modelling based on a combination of quantum beam analysis suggests that the local coordination of Ag cations is similar to that of Ag(III), which is confirmed by density functional theory calculations. This demonstration of Ag(III) in glass overturns the conventional understanding of the RPL mechanism of silver cations, redefining the science of silver-related materials.

Full automation of point defect detection in transition metal dichalcogenides through a dual mode deep learning algorithm.

Point defects often appear in two-dimensional (2D) materials and are mostly correlated with physical phenomena. The direct visualisation of point defects, followed by statistical inspection, is the most promising way to harness structure-modulated 2D materials. Here, we introduce a deep learning-based platform to identify the point defects in 2H-MoTe2: synergy of unit cell detection and defect classification. These processes demonstrate that segmenting the detected hexagonal cell into two unit cells elaborately cropped the unit cells: further separating a unit cell input into the Te2/Mo column part remarkably increased the defect classification accuracies. The concentrations of identified point defects were 7.16 × 1020 cm2 of Te monovacancies, 4.38 × 1019 cm2 of Te divacancies and 1.46 × 1019 cm2 of Mo monovacancies generated during an exfoliation process for TEM sample-preparation. These revealed defects correspond to the n-type character mainly originating from Te monovacancies, statistically. Our deep learning-oriented platform combined with atomic structural imaging provides the most intuitive and precise way to analyse point defects and, consequently, insight into the defect-property correlation based on deep learning in 2D materials.

A lithium superionic conductor for millimeter-thick battery electrode.

No design rules have yet been established for producing solid electrolytes with a lithium-ion conductivity high enough to replace liquid electrolytes and expand the performance and battery configuration limits of current lithium ion batteries. Taking advantage of the properties of high-entropy materials, we have designed a highly ion-conductive solid electrolyte by increasing the compositional complexity of a known lithium superionic conductor to eliminate ion migration barriers while maintaining the structural framework for superionic conduction. The synthesized phase with a compositional complexity showed an improved ion conductivity. We showed that the highly conductive solid electrolyte enables charge and discharge of a thick lithium-ion battery cathode at room temperature and thus has potential to change conventional battery configurations.

Prediction of the Ground-State Electronic Structure from Core-Loss Spectra of Organic Molecules by Machine Learning.

The core-loss spectrum reflects the partial density of states (PDOS) of the unoccupied states at the excited state and is a powerful analytical technique to investigate local atomic and electronic structures of materials. However, various molecular properties governed by the ground-state electronic structure of the occupied orbital cannot be directly obtained from the core-loss spectra. Here, we constructed a machine learning model to predict the ground-state carbon s- and p-orbital PDOS in both occupied and unoccupied states from the C K-edge spectra. We also attempted an extrapolation prediction of the PDOS of larger molecules using a model trained by smaller molecules and found that the extrapolation prediction performance can be improved by excluding tiny molecules. Besides, we found that using smoothing preprocess and training by specific noise data can improve the PDOS prediction for noise-contained spectra, which pave a way for the application of the prediction model to the experimental data.

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.

Nanoscale Investigation of Local Thermal Expansion at SrTiO3 Grain Boundaries by Electron Energy Loss Spectroscopy.

The presence of grain boundaries (GBs) has a great impact on the coefficient of thermal expansion (CTE) of polycrystals. However, direct measurement of local expansion of GBs remains challenging for conventional methods due to the lack of spatial resolution. In this work, we utilized the valence electron energy loss spectroscopy (EELS) in a scanning transmission electron microscope (STEM) to directly measure the CTE of Σ5 and 45°GBs of SrTiO3 at a temperature range between 373 and 973 K. A CTE that was about 3 times larger was observed in Σ5 GB along the direction normal to GB plane, while only a 1.4 time enhancement was found in the 45° GB. Our result provides direct evidence that GBs contribute to the enhancement of CTE in polycrystals. Also, this work has revealed how thermodynamic properties are varied in different GB structures and demonstrated the potential of EELS for probing local thermal properties with nanometer-scale resolution.

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.

Quantum Deep Descriptor: Physically Informed Transfer Learning from Small Molecules to Polymers.

In this study, we propose a physically informed transfer learning approach for materials informatics (MI) using a quantum deep descriptor (QDD) obtained from the quantum deep field (QDF). The QDF is a machine learning model based on density functional theory (DFT) and can be trained with a large database of molecular properties. The pre-trained QDF model can provide an effective molecular descriptor that encodes the fundamental quantum-chemical characteristics (i.e., the wave function or orbital, electron density, and energies of a molecule) learned from the large database; we refer to this descriptor as a QDD. We show that a QDD pre-trained with certain properties of small molecules can predict different properties (e.g., the band gap and dielectric constant) of polymers compared with some existing descriptors. We believe that our DFT-based, physically informed transfer learning approach will not only be useful for practical applications in MI but will also provide quantum-chemical insights into materials in the future. All codes used in this study are available at https://github.com/masashitsubaki.

Robotic fabrication of high-quality lamellae for aberration-corrected transmission electron microscopy.

Aberration-corrected scanning transmission electron microscopy (STEM) is widely used for atomic-level imaging of materials but severely requires damage-free and thin samples (lamellae). So far, the preparation of the high-quality lamella from a bulk largely depends on manual processes by a skilled operator. This limits the throughput and repeatability of aberration-corrected STEM experiments. Here, inspired by the recent successes of "robot scientists", we demonstrate robotic fabrication of high-quality lamellae by focused-ion-beam (FIB) with automation software. First, we show that the robotic FIB can prepare lamellae with a high success rate, where the FIB system automatically controls rough-milling, lift-out, and final-thinning processes. Then, we systematically optimized the FIB parameters of the final-thinning process for single crystal Si. The optimized Si lamellae were evaluated by aberration-corrected STEM, showing atomic-level images with 55 pm resolution and quantitative repeatability of the spatial resolution and lamella thickness. We also demonstrate robotic fabrication of high-quality lamellae of SrTiO3 and sapphire, suggesting that the robotic FIB system may be applicable for a wide range of materials. The throughput of the robotic fabrication was typically an hour per lamella. Our robotic FIB will pave the way for the operator-free, high-throughput, and repeatable fabrication of the high-quality lamellae for aberration-corrected STEM.

Dataset on structure and physical properties of stable diatomic systems based on van der Waals density functional method.

With the influence of progress in the materials informatics, development of fundamental database has been attracting growing interest. The bonding between atoms is essential component of all kinds of materials and govern their structure, stability, and properties. When we try to understand a material by breaking it down into microscopic components, bonding of diatomic system is the most fundamental. In the field of spectroscopy, diatomic molecular spectroscopy data has been studied well, and the diatomic molecular spectroscopy database [1] has been constructed recently. Concerning electronic structure, however, there is no easily accessible database of diatomic system. In order to develop a database of diatomic systems, it is important to consider adequate interaction. In addition to covalent bonding, van der Waals (vdW) interaction is also known to play an essential role especially in describing weak bonding systems such as noble gas dimers, atomic or molecular absorption, and layered materials. Thus, vdW interaction must be considered to develop database of diatomic systems so that it can be used for general purposes. One of its theoretical implementations is vdW density functional (vdW-DF) method [2], which has been developed within the framework of density functional theory 3 (DFT) and has been showing its effectiveness as general-purpose method. In this data article, we provide a vdW-DF-based calculation dataset focusing on diatomic systems. All diatomic systems containing atoms from H (Z = 1) to Ra (Z = 88) were considered, and stable structures and properties of more than 2,900 stable diatomic systems has been calculated correctly. This cyclopedic dataset of diatomic systems with consideration of vdW interaction can be useful building blocks for understanding, describing, and predicting interaction of atoms.

Quantum Deep Field: Data-Driven Wave Function, Electron Density Generation, and Atomization Energy Prediction and Extrapolation with Machine Learning.

Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn-Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for KS-DFT must not only predict the properties but also provide the electron density of a molecule. This Letter presents the quantum deep field (QDF), which provides the electron density with an unsupervised but end-to-end physics-informed modeling by learning the atomization energy on a large-scale dataset. QDF performed well at atomization energy prediction, generated valid electron density, and demonstrated extrapolation.

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 L2,3-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.

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.

Atomic-scale investigation of the heterogeneous structure and ionic distribution in an ionic liquid using scanning transmission electron microscopy.

Ionic liquids show characteristic properties derived from them being composed of only molecular ions, and have recently been used as solvents for chemical reactions and as electrolytes for electrochemical devices. The liquid structures, i.e., ionic distributions, form when solutes are dissolved in ionic liquids and fundamentally affect the reactions and transfer efficiency in such solutions. In this study, we directly observe the liquid structure in a solution of the long-chain ionic liquid 1-octyl-3-methylimidazolium bromide (C8mim Br) and barium stearate (Ba(C17H35COO)2) using the annular dark-field method of scanning transmission electron microscopy (ADF-STEM). The ADF image shows a 10 nm-scale heterogeneity in the image intensity, which reflects the heterogeneous ionic distribution in the solution. The number density distributions of all the component ions (C8mim+, Br-, Ba2+, and C17H35COO-) were estimated from the ADF image intensity and then visualized. These ionic distribution maps depicted the spatial relationships between the ions at the sub-nanometer scale and revealed that the heterogeneity is largely derived from the large differences in size, charge distributions, and van der Waals interactions.

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.