Computational discovery of fast interstitial oxygen conductors.

New highly oxygen-active materials may enhance many energy-related technologies by enabling efficient oxygen-ion transport at lower temperatures, for example, below ~400 °C. Interstitial oxygen conductors have the potential to realize such performance but have received far less attention than vacancy-mediated conductors. Here we combine physically motivated structure and property descriptors, ab initio simulations and experiments to demonstrate an approach to discover new fast interstitial oxygen conductors. Multiple new families were found, which adopt completely different structures from known oxygen conductors. From these families, we synthesized and studied oxygen kinetics in La4Mn5Si4O22+δ, a representative member of the perrierite/chevkinite family. We found that La4Mn5Si4O22+δ has higher oxygen-ion conductivity than the widely used yttria-stabilized ZrO2, and among the highest surface oxygen exchange rates at the intermediate temperature of known materials. The fast oxygen kinetics is the result of simultaneously active interstitial and interstitialcy diffusion pathways. We propose that the essential features for forming an effective interstitial oxygen conductor are the availability of electrons and structural flexibility, enabling a sufficient accessible volume. This work provides a powerful approach for understanding and discovering new interstitial oxygen conductors.

How close are the classical two-body potentials to ab initio calculations? Insights from linear machine learning based force matching.

In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from ab initio calculations in amorphous structures. The local feature representation is specifically chosen to make the linear weights a force field as a force/potential function of the atom pair distance. Consequently, this set of functions is the closest representation of the ab initio forces, given the two-body approximation and finite scanning in the configurational space. We validate this approach in amorphous silica. Potentials in the new force field (consisting of tabulated Si-Si, Si-O, and O-O potentials) are significantly different than existing potentials that are commonly used for silica, even though all of them produce the tetrahedral network structure and roughly similar glass properties. This suggests that the commonly used classical force fields do not offer fundamentally accurate representations of the atomic interaction in silica. The new force field furthermore produces a lower glass transition temperature (Tg ∼ 1800 K) and a positive liquid thermal expansion coefficient, suggesting the extraordinarily high Tg and negative liquid thermal expansion of simulated silica could be artifacts of previously developed classical potentials. Overall, the proposed approach provides a fundamental yet intuitive way to evaluate two-body potentials against ab initio calculations, thereby offering an efficient way to guide the development of classical force fields.

Electronic Structure-Based Descriptors for Oxide Properties and Functions.

ConspectusThe transition from fossil fuels to renewable energy requires the development of efficient and cost-effective energy storage technologies. A promising way forward is to harness the energy of intermittent renewable sources, such as solar and wind, to perform (electro)catalytic reactions to generate fuels, thus storing energy in the form of chemical bonds. However, current catalysts rely on the use of expensive, rare, or geographically localized elements, such as platinum. Widespread adoption of new (electro)catalytic technologies hinges on the discovery and development of materials containing earth-abundant elements, which can efficiently catalyze an array of (electro)chemical reactions.In the context of catalysis, descriptors provide correlations between fundamental physical properties, such as the electronic structure, and the resulting catalytic activity. The use of easily accessible descriptors has proven to be a powerful method to advance and accelerate discovery and design of new catalyst materials. The position of the oxygen electronic 2p band center has been proposed to capture the basic physical properties of oxides, including oxygen vacancy formation energy, diffusion barrier of oxygen ions, and work function. Moreover, the adsorption strength of relevant reaction intermediates at the surface of oxides can be strongly correlated with the energy of the oxygen 2p states, which affects the catalytic activity of reactions, such as oxygen electrocatalysis, and oxidative dehydrogenation of organic molecules. Such descriptors for catalytic activity can be used to predict the activity of new catalysts and understand trends and behavior among different catalysts.In this Account, we discuss how the energy of the oxygen 2p states can be used as a descriptor for oxide bulk and surface chemical properties. We show how the oxide redox properties vary linearly with the position of the oxygen 2p band center with respect to the Fermi level, and we discuss how this descriptor can be expanded across different materials and structural families, including possible generalizations to compounds outside oxides. We highlight the power of the oxygen 2p band center to predict the catalytic activity of oxides. We conclude with an outlook examining under which conditions this descriptor can be applied to predict oxide properties and possible opportunities for further refining and accelerating property predictions of oxides by leveraging material databases and machine learning.

Benchmark Tests of Atom Segmentation Deep Learning Models with a Consistent Dataset.

The information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are high performance, computationally efficient methods to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models.

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose.

Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e-/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Understanding the Fragile-to-Strong Transition in Silica from Microscopic Dynamics.

In this work, we revisit the fragile-to-strong transition (FTS) in the simulated BKS silica from the perspective of microscopic dynamics in an effort to elucidate the dynamical behaviors of fragile and strong glass-forming liquids. Softness, which is a machine-learned feature from local atomic structures, is used to predict the microscopic activation energetics and long-term dynamics. The FTS is found to originate from a change in the temperature dependence of the microscopic activation energetics. Furthermore, results suggest there are two diffusion channels with different energy barriers in BKS silica. The fast dynamics at high temperatures is dominated by the channel with small energy barriers (<∼1 eV), which is controlled by the short-range order. The rapid closing of this diffusion channel when lowering temperature leads to the fragile behavior. On the other hand, the slow dynamics at low temperatures is dominated by the channel with large energy barriers controlled by the medium-range order. This slow diffusion channel changes only subtly with temperature, leading to the strong behavior. The distributions of barriers in the two channels show different temperature dependences, causing a crossover at ∼3100 K. This transition temperature in microscopic dynamics is consistent with the inflection point in the configurational entropy, suggesting there is a fundamental correlation between microscopic dynamics and thermodynamics.

Surface Diffusion Is Controlled by Bulk Fragility across All Glass Types.

Surface diffusion is vastly faster than bulk diffusion in some glasses, but only moderately enhanced in others. We show that this variation is closely linked to bulk fragility, a common measure of how quickly dynamics is excited when a glass is heated to become a liquid. In fragile molecular glasses, surface diffusion can be a factor of 10^{8} faster than bulk diffusion at the glass transition temperature, while in the strong system SiO_{2}, the enhancement is a factor of 10. Between these two extremes lie systems of intermediate fragility, including metallic glasses and amorphous selenium and silicon. This indicates that stronger liquids have greater resistance to dynamic excitation from bulk to surface and enables prediction of surface diffusion, surface crystallization, and formation of stable glasses by vapor deposition.

CO Oxidation Catalytic Effects of Intrinsic Surface Defects in Rhombohedral LaMnO3.

There is an ongoing effort to replace rare and expensive noble-element catalysts with more abundant and less expensive transition metal oxides. With this goal in mind, the intrinsic defects of a rhombohedral perovskite-like structure of LaMnO3 and their implications on CO catalytic properties were studied. Surface thermodynamic stability as a function of pressure (P) and temperature (T) were calculated to find the most stable surface under reaction conditions (P=0.2 atm, T=323 K to 673 K). Crystallographic planes (100), (111), (110), and (211) were evaluated and it was found that (110) with MnO2 termination was the most stable under reaction conditions. Adsorption energies of O2 and CO on (110) as well as the effect of intrinsic defects such as Mn and O vacancies were also calculated. It was found that O vacancies favor the interaction of CO on the surface, whereas Mn vacancies can favor the formation of carbonate species.

Machine learning for impurity charge-state transition levels in semiconductors from elemental properties using multi-fidelity datasets.

Quantifying charge-state transition energy levels of impurities in semiconductors is critical to understanding and engineering their optoelectronic properties for applications ranging from solar photovoltaics to infrared lasers. While these transition levels can be measured and calculated accurately, such efforts are time-consuming and more rapid prediction methods would be beneficial. Here, we significantly reduce the time typically required to predict impurity transition levels using multi-fidelity datasets and a machine learning approach employing features based on elemental properties and impurity positions. We use transition levels obtained from low-fidelity (i.e., local-density approximation or generalized gradient approximation) density functional theory (DFT) calculations, corrected using a recently proposed modified band alignment scheme, which well-approximates transition levels from high-fidelity DFT (i.e., hybrid HSE06). The model fit to the large multi-fidelity database shows improved accuracy compared to the models trained on the more limited high-fidelity values. Crucially, in our approach, when using the multi-fidelity data, high-fidelity values are not required for model training, significantly reducing the computational cost required for training the model. Our machine learning model of transition levels has a root mean squared (mean absolute) error of 0.36 (0.27) eV vs high-fidelity hybrid functional values when averaged over 14 semiconductor systems from the II-VI and III-V families. As a guide for use on other systems, we assessed the model on simulated data to show the expected accuracy level as a function of bandgap for new materials of interest. Finally, we use the model to predict a complete space of impurity charge-state transition levels in all zinc blende III-V and II-VI systems.

Radiation-induced segregation in a ceramic.

Radiation-induced segregation is well known in metals, but has been rarely studied in ceramics. We discover that radiation can induce notable segregation of one of the constituent elements to grain boundaries in a ceramic, despite the fact that the ceramic forms a line compound and therefore has a strong thermodynamic driving force to resist off-stoichiometry. Specifically, irradiation of silicon carbide at 300 °C leads to carbon enrichment near grain boundaries, whereas the enrichment diminishes for irradiation at 600 °C. The temperature dependence of this radiation-induced segregation is different from that shown in metallic systems. Using an ab initio informed rate theory model, we demonstrate that this difference is introduced by the unique defect energy landscapes present in the covalent system. Additionally, we discover that grain boundaries in unirradiated silicon carbide grown by chemical vapour deposition are intrinsically carbon-depleted. The inherent grain boundary chemistry and its evolution under radiation are both critical for understanding the many properties of ceramics associated with grain boundaries.

Factors correlating to enhanced surface diffusion in metallic glasses.

The enhancement of surface diffusion (DS) over the bulk (DV) in metallic glasses (MGs) is well documented and likely to strongly influence the properties of glasses grown by vapor deposition. Here, we use classical molecular dynamics (MD) simulations to identify different factors influencing the enhancement of surface diffusion in MGs. MGs have a simple atomic structure and belong to the category of moderately fragile glasses that undergo pronounced slowdown of bulk dynamics with cooling close to the glass transition temperature (Tg). We observe that DS exhibits a much more moderate slowdown compared to DV when approaching Tg, and DS/DV at Tg varies by two orders of magnitude among the MGs investigated. We demonstrate that both the surface energy and the fraction of missing bonds for surface atoms show good correlation to DS/DV, implying that the loss of nearest neighbors at the surface directly translates into higher mobility, unlike the behavior of network-bonded and hydrogen-bonded organic glasses. Fragility, a measure of the slowdown of bulk dynamics close to Tg, also correlates to DS/DV, with more fragile systems having larger surface enhancement of mobility. The deviations observed in the fragility-DS/DV relationship are shown to be correlated to the extent of segregation or depletion of the mobile element at the surface. Finally, we explore the relationship between the diffusion pre-exponential factor (D0) and the activation energy (Q) and compare it to a ln(D0)-Q correlation previously established for bulk glasses, demonstrating similar correlations from MD as in the experiments and that the surface and bulk have very similar ln(D0)-Q correlations.

Graph network based deep learning of bandgaps.

Recent machine learning models for bandgap prediction that explicitly encode the structure information to the model feature set significantly improve the model accuracy compared to both traditional machine learning and non-graph-based deep learning methods. The ongoing rapid growth of open-access bandgap databases can benefit such model construction not only by expanding their domain of applicability but also by requiring constant updating of the model. Here, we build a new state-of-the-art multi-fidelity graph network model for bandgap prediction of crystalline compounds from a large bandgap database of experimental and density functional theory (DFT) computed bandgaps with over 806 600 entries (1500 experimental, 775 700 low-fidelity DFT, and 29 400 high-fidelity DFT). The model predicts bandgaps with a 0.23 eV mean absolute error in cross validation for high-fidelity data, and including the mixed data from all different fidelities improves the prediction of the high-fidelity data. The prediction error is smaller for high-symmetry crystals than for low symmetry crystals. Our data are published through a new cloud-based computing environment, called the "Foundry," which supports easy creation and revision of standardized data structures and will enable cloud accessible containerized models, allowing for continuous model development and data accumulation in the future.

Shape Dependence of Pressure-Induced Phase Transition in CdS Semiconductor Nanocrystals.

Understanding structural stability and phase transformation of nanoparticles under high pressure is of great scientific interest, as it is one of the crucial factors for design, synthesis, and application of materials. Even though high-pressure research on nanomaterials has been widely conducted, their shape-dependent phase transition behavior still remains unclear. Examples of phase transitions of CdS nanoparticles are very limited, despite the fact that it is one of the most studied wide band gap semiconductors. Here we have employed in situ synchrotron wide-angle X-ray scattering and transmission electron microscopy (TEM) to investigate the high-pressure behaviors of CdS nanoparticles as a function of particle shapes. We observed that CdS nanoparticles transform from wurtzite to rocksalt phase at elevated pressure in comparison to their bulk counterpart. Phase transitions also vary with particle shape: rod-shaped particles show a partially reversible phase transition and the onset of the structural phase transition pressure decreases with decreasing surface-to-volume ratios, while spherical particles undergo irreversible phase transition with relatively low phase transition pressure. Additionally, TEM images of spherical particles exhibited sintering-induced morphology change after high-pressure compression. Calculations of the bulk modulus reveal that spheres are more compressible than rods in the wurtzite phase. These results indicate that the shape of the particle plays an important role in determining their high-pressure properties. Our study provides important insights into understanding the phase-structure-property relationship, guiding future design and synthesis of nanoparticles for promising applications.

Density Functional Theory Study of the Gas Phase and Surface Reaction Kinetics for the MOVPE Growth of GaAs1-yBiy.

The kinetics of chemical reactions occurring during the metal-organic vapor phase epitaxy (MOVPE) of GaAs1-yBiy have been studied using density functional theory (DFT). GaAs1-yBiy is a metastable semiconductor alloy that has potential applications in high-performance long-wavelength emitters. Its growth is complicated by the low solubility of Bi within the GaAs lattice, which leads to phase segregation under conventional III-V semiconductor growth conditions. In this study, the thermochemical and kinetic parameters of the gas-phase pyrolysis and surface reactions occurring in the MOVPE growth of GaAs1-yBiy from trimethyl bismuth, tertiary butyl arsine, and triethyl gallium are calculated from first-principles electronic structure and vibrational mode calculations. These calculations indicate that the pyrolysis products AsH2 and Bi(CH3)2 are the principle sources for the deposition of their respective metallic elements. The surface-adsorbed methyl species and their interaction with the gas-phase pyrolysis products lead to the self-limiting growth described within this model. The calculated thermochemical and kinetic values provide initial parameters for the development of a microkinetic model of GaAs1-yBiy deposition.

Stability of ferrous-iron-rich bridgmanite under reducing midmantle conditions.

Our current understanding of the electronic state of iron in lower-mantle minerals leads to a considerable disagreement in bulk sound speed with seismic measurements if the lower mantle has the same composition as the upper mantle (pyrolite). In the modeling studies, the content and oxidation state of Fe in the minerals have been assumed to be constant throughout the lower mantle. Here, we report high-pressure experimental results in which Fe becomes dominantly Fe2+ in bridgmanite synthesized at 40-70 GPa and 2,000 K, while it is in mixed oxidation state (Fe3+/∑Fe = 60%) in the samples synthesized below and above the pressure range. Little Fe3+ in bridgmanite combined with the strong partitioning of Fe2+ into ferropericlase will alter the Fe content for these minerals at 1,100- to 1,700-km depths. Our calculations show that the change in iron content harmonizes the bulk sound speed of pyrolite with the seismic values in this region. Our experiments support no significant changes in bulk composition for most of the mantle, but possible changes in physical properties and processes (such as viscosity and mantle flow patterns) in the midmantle.

Massive Vacancy Concentration Yields Strong Room-Temperature Ferromagnetism in Two-Dimensional ZnO.

Two-dimensional (2D) ZnO nanosheets with highly concentrated Zn vacancies (VZn) of up to approximately 33% were synthesized by ionic layer epitaxy at the water-toluene interface. This high cation vacancy concentration is unprecedented for ZnO and may provide unique opportunities to realize exotic properties not attainable in the conventional bulk form. After annealing, the nanosheets showed characteristic magnetic hysteresis with saturation magnetization of 57.2 emu/g at 5 K and 50.9 emu/g at room temperature. This value is 1 order of magnitude higher than other ZnO nanostructures and comparable to the conventional ferrimagnetic Fe3O4. Density functional theory calculations, with the support of experimental results, suggest that a high concentration of VZn (approximately one-third of the Zn sites) can form spontaneously during synthesis when stabilized by H ions, and the formation of VZn could be further facilitated by the presence of grain boundaries. It is essential to remove the H for the nanosheets to show ferromagnetism. The mechanisms identified for the origin of the high magnetism in ZnO nanosheets presents an intriguing example of a kinetically stabilized, non-equilibrium, highly defective 2D nanomaterial with a significantly enhanced physical property.

The O-O Bonding and Hydrogen Storage in the Pyrite-type PtO2.

We have synthesized pyrite-type PtO2 (py-PtO2) at 50-60 GPa and successfully recovered it at 1 bar. The observed O-O stretching vibration in Raman spectra provides direct evidence for inter-oxygen bonding in the structure. We also identified the O-H vibrations in py-PtO2 synthesized from the low-temperature areas, indicating hydrogenation, py-PtO2H x ( x ≤ 1). Diffraction patterns are consistent with a range of degrees of hydrogenation controlled by temperature. We found that py-PtO2 has a high bulk modulus, 314 ± 4 GPa. The chemical behaviors found in py-PtO2 have implications for the hydrogen storage in materials with anion-anion bonding, and the geochemistry of oxygen, hydrogen, and transition metals in the deep planetary interiors.