Fast evaluation technique for the shear viscosity and ionic conductivity of electrolyte solutions.

With the growing need to obtain ideal materials for various applications, there is an increasing interest in computational methods to rapidly and accurately search for materials. Molecular dynamics simulation is one of the successful methods used to investigate liquid electrolytes with high transport properties applied in lithium-ion batteries. However, further reduction in computational cost is required to find a novel material with the desired properties from a large number of combinations. In this study, we demonstrate an effective fast evaluation technique for shear viscosity and ionic conductivity by molecular dynamics simulation for an exhaustive search of electrolyte materials with high transport properties. The proposed model was combined with a short-time correlation function of the stress tensor and empirical relationships to address the issues of inefficient and uncertain evaluation by conventional molecular dynamics methods. Because we focus on liquid electrolytes consisting of organic solvents and lithium salts, our model requires dissociation ratio and effective diffusion size of lithium salts. Our method is applied to search for the compositional combinations of electrolytes with superior transport properties even at low temperatures. These results correlate well with experimental results.

Synthesis and Ion-Transport Properties of EuKGe2O6-, Ca3Fe2Ge3O12-, and BaCu2Ge2O7-Type Oxide-Ion Conductors.

EuKGe2O6-, Ca3Fe2Ge3O12-, and BaCu2Ge2O7-type germanates are synthesized by a conventional solid-state method and characterized to reveal their oxide-ion-conducting properties. Materials of the EuKGe2O6 group exhibit oxide-ion conductivity (e.g., 4.6 × 10-3 S/cm at 973 K for Eu0.8Ca0.2KGe2O6-δ) and transport numbers above 96%, whereas materials of the Ca3Fe2Ge3O12 and BaCu2Ge2O7 groups exhibit mixed electron-/oxide-ion conduction. Conduction involves oxide-ion vacancies in the EuKGe2O6 group, interstitial oxide ions in the Ca3Fe2Ge3O12 group, and both oxide-ion vacancies and interstitial oxide ions in the BaCu2Ge2O7 group. The doping-induced formation of impurity phases decreases the amount of oxide-ion carriers relative to the expected values.

Mechanism of monolayer to bilayer silicene transformation in CaSi2 due to fluorine diffusion.

Calcium disilicide (CaSi2) possesses a layered structure composed of alternating monolayers of silicene (MLSi) and calcium. Here the mechanism by which fluorine (F) diffusion into CaSi2 leads to a phase transformation from MLSi to bilayer silicene (BLSi) was investigated. Disorder in intra-layer atomic arrangements and F aggregation were observed using HAADF-STEM in areas of low F concentration. Transformation of MLSi to BLSi in CaSi2Fx was predicted to occur at x = 0.63 based on cluster expansion (CE) and density functional theory (DFT) analyses, and these results agreed well with HAADF-STEM observations. The occurrence of F aggregation at low concentrations was also confirmed by Monte Carlo simulations using the interaction parameters obtained in CE analysis. Bader charge analysis, DFT calculations of charged states, and ab initio molecular dynamics simulations indicated that the aggregated F atoms withdrew electrons from MLSi, destabilizing the buckled honeycomb structure of MLSi in CaSi2. This charge imbalance caused the transformation of MLSi to the covalent-like BLSi.

Lithium-ion attack on yttrium oxide in the presence of copper powder during Li plating in a super-concentrated electrolyte.

Li plating/stripping on Cu and Y2O3 (Cu + Y2O3) electrodes was examined in a super-concentrated electrolyte of lithium bis(fluorosulfonyl)amide and methylphenylamino-di(trifluoroethyl) phosphate. In principle, Li+ ions cannot intercalate into a Y2O3 crystal because its intercalation potential obtained from first-principles calculations is -1.02 V vs. Li+/Li. However, a drastic decrease in the electrode potential and a subsequent constant-potential region were observed during Li plating onto a Cu + Y2O3 electrode, suggesting that Li+ interacted with Y2O3. X-ray diffraction (XRD) patterns and X-ray absorption fine structure (XAFS) spectra of the Cu + Y2O3 electrodes after the Li plating were recorded to verify this phenomenon. The XRD and XAFS results indicated that the crystallinity of Y2O3 crystals was lowered because of attack by Li+ ions or that the Y2O3 crystal structure was broken while the +3 valence state of Y was maintained.

CO oxidation activity of non-reducible oxide-supported mass-selected few-atom Pt single-clusters.

Platinum nanocatalysts play critical roles in CO oxidation, an important catalytic conversion process. As the catalyst size decreases, the influence of the support material on catalysis increases which can alter the chemical states of Pt atoms in contact with the support. Herein, we demonstrate that under-coordinated Pt atoms at the edges of the first cluster layer are rendered cationic by direct contact with the Al2O3 support, which affects the overall CO oxidation activity. The ratio of neutral to cationic Pt atoms in the Pt nanocluster is strongly correlated with the CO oxidation activity, but no correlation exists with the total surface area of surface-exposed Pt atoms. The low oxygen affinity of cationic Pt atoms explains this counterintuitive result. Using this relationship and our modified bond-additivity method, which only requires the catalyst-support bond energy as input, we successfully predict the CO oxidation activities of various sized Pt clusters on TiO2.

First-principles prediction of high oxygen-ion conductivity in trilanthanide gallates Ln3GaO6.

We systematically investigated trilanthanide gallates (Ln3GaO6) with the space group Cmc21 as oxygen-ion conductors using first-principles calculations. Six Ln3GaO6 (Ln = Nd, Gd, Tb, Ho, Dy, or Er) are both energetically and dynamically stable among 15 Ln3GaO6 compounds, which is consistent with previous experimental studies reporting successful syntheses of single phases. La3GaO6 and Lu3GaO6 may be metastable despite a slightly higher energy than those of competing reference states, as phonon calculations predict them to be dynamically stable. The formation and the migration barrier energies of an oxygen vacancy (V O) suggest that eight Ln3GaO6 (Ln = La, Nd, Gd, Tb, Ho, Dy, Er, or Lu) can act as oxygen-ion conductors based on V O. Ga plays a role of decreasing the distances between the oxygen sites of Ln3GaO6 compared with those of Ln2O3 so that a V O migrates easier with a reduced migration barrier energy. Larger oxygen-ion diffusivities and lower migration barrier energies of V O for the eight Ln3GaO6 are obtained for smaller atomic numbers of Ln having larger radii of Ln3+. Their oxygen-ion conductivities at 1000 K are predicted to have a similar order of magnitude to that of yttria-stabilized zirconia.

Oxygen conduction mechanism in Ca3Fe2Ge3O12 garnet-type oxide.

We investigate the oxygen conduction mechanism in a garnet-type oxide, Ca3Fe2Ge3O12, for the first time in detail by first-principle calculations. The nudged elastic band results confirm that this oxide has a lower migration barrier energy (0.45 eV) for an oxygen interstitial (Oi) with the kick-out mechanism than that (0.76 eV) for an oxygen vacancy. The migration paths for Oi are delocalized and connected to the neighboring cells in three-dimensional space. This oxide does not have a very low formation energy of Oi when the Fermi level is near the lowest unoccupied molecular orbital at a high temperature, which implies the possibility of electron doping by high-valence cations. These theoretical results suggest that the doping of Ca3Fe2Ge3O12 for generation of excess Oi provides a good oxygen-ion conductivity, along with the electronic conductivity.

Search for high-capacity oxygen storage materials by materials informatics.

Oxygen storage materials (OSMs), such as pyrochlore type CeO2-ZrO2 (p-CZ), are used as a catalyst support for three-way catalysts in automotive emission control systems. They have oxygen storage capacity (OSC), which is the ability to release and store oxygen reversibly by the fluctuation of cation oxidation states depending on the reducing or oxidizing atmosphere. In this study, we explore high-capacity OSMs by using materials informatics (MI) combining experiments, first-principles calculations, and machine learning (ML). To generate training data for the ML model, the OSC values of 60 metal oxides were measured from the amount of CO2 produced under alternating flow gas between oxidizing (O2) and reducing (CO) conditions at 973, 773, and 573 K. Descriptors were computed by atomic properties and first-principles calculations on each oxide. The support vector machine regression model was trained to predict the OSC at each temperature. The features describing OSC were automatically selected using grid search to achieve practical cross validation performance. The features related to the stability of the oxygen atoms in the crystal and the crystal structure itself such as cohesive energy are highly correlated with OSC. The present model predicts the OSC of 1300 existing oxides. Based on its high predictive power for OSC and synthesizability, we focused on Cu3Nb2O8. We synthesized this material and experimentally confirmed that Cu3Nb2O8 showed a higher OSC than conventional OSM p-CZ. This MI scheme can significantly accelerate the development of new OSMs.

Discovery of zirconium dioxides for the design of better oxygen-ion conductors using efficient algorithms beyond data mining.

It is important to find crystal structures with low formation (E v) and migration-barrier (E m) energies for oxygen vacancies for the development of fast oxygen-ion conductors. To identify crystal structures with lower E v and E m than those of ground-state ZrO2, we first reoptimize the crystal structures of various oxides reported in the database, and then directly construct them using an evolutionary algorithm. For efficient searching, we employ the linearized ridge regression model for E v using descriptors obtained from density functional theory calculations of the unit cells and apply the predicted E v as a fitness value in the evolutionary algorithm. We also find a correlation between the E v and E m for the crystal structures of ZrO2. On the basis of this correlation, we confirm that the newly constructed crystal structures, as well as certain reoptimized structures from the database, that possess low E v also have E m lower than that of ground-state ZrO2. Our successful strategy consisting of a combination of the evolutionary algorithm, first-principles calculations, and machine-learning techniques may be applicable to other oxide systems in finding crystal structures with low E v and E m.

A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks.

Material informatics (MI) is a promising approach to liberate us from the time-consuming Edisonian (trial and error) process for material discoveries, driven by machine-learning algorithms. Several descriptors, which are encoded material features to feed computers, were proposed in the last few decades. Especially to solid systems, however, their insufficient representations of three dimensionality of field quantities such as electron distributions and local potentials have critically hindered broad and practical successes of the solid-state MI. We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in such a suitable way to implement convolutional neural networks (CNNs). We examine the 3D voxel descriptor encoded from the electron distribution by a regression test with 680 oxides data. The present scheme outperforms other existing descriptors in the prediction of Hartree energies that are significantly relevant to the long-wavelength distribution of the valence electrons. The results indicate that this scheme can forecast any functionals of field quantities just by learning sufficient amount of data, if there is an explicit correlation between the target properties and field quantities. This 3D descriptor opens a way to import prominent CNNs-based algorithms of supervised, semi-supervised and reinforcement learnings into the solid-state MI.

On/off switchable electronic conduction in intercalated metal-organic frameworks.

The electrical properties of metal-organic frameworks (MOF) have attracted attention for MOF as electronic materials. We report on/off switchable electronic conduction behavior with thermal responsiveness in intercalated MOF (iMOF) with layered structure, 2,6-naphthalene dicarboxylate dilithium, which was previously reported as a reversible Li-intercalation electrode material. The I-V response of the intercalated sample, which was prepared using a chemically reductive lithiation agent, exhibits current flow with sufficiently high electronic conductivity, even though it displays insulating characteristics in the pristine state. Calculations of band structure and electron hopping conduction indicate that electronic conduction occurs in the two-dimensional π-stacking naphthalene layers when the band gap is decreased to 0.99 eV and because of the formation of an anisotropic electron hopping conduction pathway by Li intercalation. The structure exhibiting electronic conductivity remains stable up to 200°C and reverts to an insulating structure, without collapsing, at 400°C, offering the potential for a shutdown switch for battery safety during thermal runaway or for heat-responsive on/off switching electronic devices.

Organic dicarboxylate negative electrode materials with remarkably small strain for high-voltage bipolar batteries.

As advanced negative electrodes for powerful and useful high-voltage bipolar batteries, an intercalated metal-organic framework (iMOF), 2,6-naphthalene dicarboxylate dilithium, is described which has an organic-inorganic layered structure of π-stacked naphthalene and tetrahedral LiO4 units. The material shows a reversible two-electron-transfer Li intercalation at a flat potential of 0.8 V with a small polarization. Detailed crystal structure analysis during Li intercalation shows the layered framework to be maintained and its volume change is only 0.33%. The material possesses two-dimensional pathways for efficient electron and Li(+) transport formed by Li-doped naphthalene packing and tetrahedral LiO3C network. A cell with a high potential operating LiNi(0.5)Mn(1.5)O4 spinel positive and the proposed negative electrodes exhibited favorable cycle performance (96% capacity retention after 100 cycles), high specific energy (300 Wh kg(-1)), and high specific power (5 kW kg(-1)). An 8 V bipolar cell was also constructed by connecting only two cells in series.

Hydrogen absorption and desorption by the Li-Al-N-H system.

Lithium hexahydridoaluminate Li(3)AlH(6) and lithium amide LiNH(2) with 1:2 molar ratio were mechanically milled, yielding a Li-Al-N-H system. LiNH(2) destabilized Li(3)AlH(6) during the dehydrogenation process of Li(3)AlH(6), because the dehydrogenation starting temperature of the Li-Al-N-H system was lower than that of Li(3)AlH(6). Temperature-programmed desorption scans of the Li-Al-N-H system indicated that a large amount of hydrogen (6.9 wt %) can be released between 370 and 773 K. After initial H(2) desorption, the H(2) absorption and the desorption capacities of the Li-Al-N-H system with a nano-Ni catalyst exhibited 3-4 wt % at 10-0.004 MPa and 473-573 K, while the capacities of the system without the catalyst were 1-2 wt %. The remarkably increased capacity was due to the fact that the kinetics was improved by addition of the nano-Ni catalyst.