Automatic knowledge acquisition from superconductivity information in literature

In this study, we developed a natural language processing model for extracting information solely from the abstracts of literature on superconducting materials, with the aim of making predictions for materials science. Using a dataset of tagged documents (annotations) on superconductivity, the DyGIE++ framework was employed for the simultaneous extraction of the named entities, relations, and events. Additionally, a model was developed for classifying the subject material in the abstracts. After training with 1,000 annotated abstracts, the model extracted information, such as the material composition, superconducting transition temperature, doping information, and process information, automatically from 48,565 abstracts registered in the Scopus database since 1937. The numbers of extracted entries concerning superconducting materials and transition temperatures were 43,944 and 24,075, respectively, i.e. equivalent to the number of entries in the existing databases. Machine learning models were constructed to predict physical and chemical properties. For example, the superconducting transition temperatures were predicted for compositions, with a mean absolute error of 15 K. In addition, the doping information indicated that the superconducting transition temperature was correlated with the choice of dopant and doping site

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 (VO) suggest that eight Ln3GaO6 (Ln = La, Nd, Gd, Tb, Ho, Dy, Er, or Lu) can act as oxygen-ion conductors based on VO. Ga plays a role of decreasing the distances between the oxygen sites of Ln3GaO6 compared with those of Ln2O3 so that a VO migrates easier with a reduced migration barrier energy. Larger oxygen-ion diffusivities and lower migration barrier energies of VO 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

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm

Catalytic activities are often dominated by a few specific surface sites, and designing active sites is the key to realize high-performance heterogeneous catalysts. The great triumphs of modern surface science lead to reproduce catalytic reaction rates by modeling the arrangement of surface atoms with well-defined single-crystal surfaces. However, this method has limitations in the case for highly inhomogeneous atomic configurations such as on alloy nanoparticles with atomic-scale defects, where the arrangement cannot be decomposed into single crystals. Here, we propose a universal machine-learning scheme using a local similarity kernel, which allows interrogation of catalytic activities based on local atomic configurations. We then apply it to direct NO decomposition on RhAu alloy nanoparticles. The proposed method can efficiently predict energetics of catalytic reactions on nanoparticles using DFT data on single crystals, and its combination with kinetic analysis can provide detailed information on structures of active sites and size- and composition-dependent catalytic activities

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

Abstract 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