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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 Al 2 O 3 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 TiO 2 . Platinum nanocatalysts play critical roles in CO oxidation. Herein, the authors discover that under-coordinated Pt atoms at the edges of the first cluster layer are rendered cationic by direct contact with the Al 2 O 3 support, which affects the overall CO oxidation activity.

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.

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.

We investigate the oxygen conduction mechanism in a garnet-type oxide, Ca 3 Fe 2 Ge 3 O 12 , 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 (O i ) with the kick-out mechanism than that (0.76 eV) for an oxygen vacancy. The migration paths for O i are delocalized and connected to the neighboring cells in three-dimensional space. This oxide does not have a very low formation energy of O i 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 Ca 3 Fe 2 Ge 3 O 12 for generation of excess O i provides a good oxygen-ion conductivity, along with the electronic conductivity.

Machine learning accelerates virtual screening in which material candidates are selected from existing databases, facilitating materials discovery in a broad chemical search space. Machine learning models quickly predict a target property from explanatory material features called descriptors. However, a major bottleneck of the machine learning model is an insufficient amount of training data in materials science, especially data with non-equilibrium properties. Here, we develop an alternative virtual-screening process via ensemble-based machine learning with one handcrafted and two generic descriptors to maximize the inference ability even using a small training dataset. A joint representation with the three descriptors translates the physical and chemical properties of a material as well as its underlying short- and long-range atomic structures to describe a multifaceted perspective of the material. As an application, the ensemble-scope descriptor learning model was trained with only 29 entries in the training dataset, and it selected potential oxygen-ion conductors from 13,384 oxides in the inorganic crystal structure database. The experiments confirmed that we successfully discovered five compounds that have not been reported, to the best of our knowledge, as oxygen-ion conductors.Materials discovery: Teaching machines to do more with lessAn improved method for training machine learning algorithms has helped researchers uncover promising electrolytes for fuel cells. A common strategy for discovering fuel cell materials is to screen chemical databases for target properties, including fast transport of oxygen ions. Seiji Kajita and colleagues from the Toyota Central R&D Labs in Nagakute, Japan have now developed a screening technique that can spot speedy ion conductors from a limited amount of information. The team trained a machine learning algorithm using generic atomic descriptions and a small, statistically vetted database of factors deemed critical to ion transport, such as crystal porosity. Using only 29 types of training data, the new algorithm screened over ten thousand different ceramic oxides and found several compounds with potentially high conductivity that have yet to be synthesized.

When a sample’s X-ray diffraction pattern (XRD) is measured, the corresponding crystal structure is usually determined by searching for similar XRD patterns in the database. However, if a similar XRD pattern is not found, it is tremendously laborious to identify the crystal structure even for experts. This case commonly happens when researchers develop novel and complex materials. In this study, we propose a crystal structure creation scheme that reproduces a given XRD pattern. We employed a combinatorial inverse design method using an evolutionary algorithm and crystal morphing (Evolv&Morph) supported by Bayesian optimization, which maximizes the similarity of the XRD patterns between target one and those of the created crystal structures. For sixteen different crystal structure systems with twelve simulated and four powder target XRD patterns, Evolv&Morph successfully created crystal structures with the same XRD pattern as the target (cosine similarity 99% for the simulated ones and >96% the experimentally measured ones). Furthermore, the present method has merits in that it is an automated crystal structure creation scheme, not dependent on a database. We believe that Evolv&Morph can be applied not only to determine crystal structures but also to design materials for specific properties.

Combinatorial chemistry has been proven effective in the search for novel functional materials, especially in the field of organic chemistry, and is being used to identify functional inorganic compounds. However, there is a growing need for approaches that predict and experimentally realize new materials, beyond composition optimization of known systems. Application of combinatorial chemistry to materials discovery is typically hindered by a limited ability to search a wide chemical composition space, and by our ability to experimentally screen promising compounds. Here, a combinatorial scheme is proposed that combines a materials informatics technique to define a chemical search space with high-throughput synthesis and evaluation. We identify high-performance superionic conductors in the Ca-(Nb,Ta)-Bi-O system, demonstrating the effectiveness of this approach for accelerated materials discovery.High-throughput prediction and synthesis are vital for obtaining new materials that deviate from existing compositions. Here, machine learning is combined with high-throughput synthesis to identify superionic conductors based on Ca-(Nb,Ta)-Bi-O.

We systematically investigated trilanthanide gallates (Ln 3 GaO 6 ) with the space group Cmc2 1 as oxygen-ion conductors using first-principles calculations. Six Ln 3 GaO 6 (Ln = Nd, Gd, Tb, Ho, Dy, or Er) are both energetically and dynamically stable among 15 Ln 3 GaO 6 compounds, which is consistent with previous experimental studies reporting successful syntheses of single phases. La 3 GaO 6 and Lu 3 GaO 6 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 Ln 3 GaO 6 (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 Ln 3 GaO 6 compared with those of Ln 2 O 3 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 Ln 3 GaO 6 are obtained for smaller atomic numbers of Ln having larger radii of Ln 3+ . Their oxygen-ion conductivities at 1000 K are predicted to have a similar order of magnitude to that of yttria-stabilized zirconia.

Hybrid capacitors should ideally exhibit high volumetric energy density, favorable low-temperature performance and safe operation. Here we describe a negative electrode comprising an intercalated metal–organic framework, 4,4′-biphenyl dicarboxylate dilithium [4,4′-Bph(COOLi) 2 ], which forms a repeating organic–inorganic layered structure of π-stacked biphenyl and tetrahedral LiO 4 units. The electrode shows a stepwise two-electron transfer and has a capacity of 190 mAh g −1 at 0.7 V vs. Li/Li + , which can suppress the lithium metal deposition reaction occurring an internal short circuit. A hybrid capacitor containing 4,4′-Bph(COOLi) 2 negative and activated carbon positive electrodes possesses high volumetric energy density of approximately 60 Wh L −1 and good high-rate performance, particularly at the low temperature of 0 °C, because of low charge-transfer resistance along with low activation energy. Hopping mobility calculations suggest the observed low resistance properties are the result of high electron mobility arising from two electron-hopping pathways between adjacent molecules in the π-stacked biphenyl packing layer by lithium intercalation. Intercalated metal-organic frameworks hold promising potential as supercapacitors. Here the performance of 4,4′-biphenyl dicarboxylate dilithium is explored using both experimental and computational methods, offering insight into the basis for high electron and lithium-ion conduction in this material.

We investigate the oxygen conduction mechanism in a garnet-type oxide, Ca Fe Ge O , 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 (O ) with the kick-out mechanism than that (0.76 eV) for an oxygen vacancy. The migration paths for O are delocalized and connected to the neighboring cells in three-dimensional space. This oxide does not have a very low formation energy of O 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 Ca Fe Ge O for generation of excess O provides a good oxygen-ion conductivity, along with the electronic conductivity.