Faculty Profiles - ASAHI Ryoji

Papers - ASAHI Ryoji

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Oxygen conduction mechanism in Ca3Fe2Ge3O12 garnet-type oxide Reviewed

Lee Joohwi, Ohba Nobuko, Asahi Ryoji

SCIENTIFIC REPORTS Vol. 9 ( 1 ) page： 2593 2019.2

Effects of doping IIIB elements (Al, Ga, In) on thermoelectric properties of nanostructured n-type filled skutterudite compounds Reviewed

Matsubara M., Masuoka Y., Asahi R.

JOURNAL OF ALLOYS AND COMPOUNDS Vol. 774 page： 731 - 738 2019.2

Solar Thermal Cogeneration System Using a Cylindrical Thermoelectric Module Reviewed

Suzumura Akitoshi, Hazama Hirofumi, Matsubara Masato, Asahi Ryoji

JOURNAL OF ELECTRONIC MATERIALS Vol. 48 ( 1 ) page： 467 - 474 2019.1

Molecular dynamics simulations with machine learning potential for Nb-doped lithium garnet-type oxide Li7-xLa3(Zr2-xNbx)O-12 Reviewed

Miwa Kazutoshi, Asahi Ryoji

PHYSICAL REVIEW MATERIALS Vol. 2 ( 10 ) 2018.10

Charge transfer dynamics at the boron subphthalocyanine chloride/C-60 interface: non-adiabatic dynamics study with Libra-X Reviewed

Sato Kosuke, Pradhan Ekadashi, Asahi Ryoji, Akimov Alexey V.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS Vol. 20 ( 39 ) page： 25275 - 25294 2018.10

Cylindrical thermoelectric generator with water heating system for high solar energy conversion efficiency Reviewed

Hazama Hirofumi, Masuoka Yumi, Suzumura Akitoshi, Matsubara Masato, Tajima Shin, Asahi Ryoji

APPLIED ENERGY Vol. 226 page： 381 - 388 2018.9

Phase-field modeling of stress generation in polycrystalline LiCoO2 Reviewed

Yamakawa Shunsuke, Nagasako Naoyuki, Yarnasaki Hisatsugu, Koyama Toshiyuki, Asahi Ryoji

SOLID STATE IONICS Vol. 319 page： 209 - 217 2018.6

Band bending and dipole effect at interface of metal-nanoparticles and TiO2 directly observed by angular-resolved hard X-ray photoemission spectroscopy Reviewed

Sato Shunsuke, Kataoka Keita, Jinnouchi Ryosuke, Takahashi Naoko, Sekizawa Keita, Kitazumi Kousuke, Ikenaga Eiji, Asahi Ryoji, Morikawa Takeshi

PHYSICAL CHEMISTRY CHEMICAL PHYSICS Vol. 20 ( 16 ) page： 11342 - 11346 2018.4

Effects of Ta2O5 Surface Modification by NH3 on the Electronic Structure of a Ru-Complex/N Ta2O5 Hybrid Photocatalyst for Selective CO2 Reduction Reviewed

Shirai Soichi, Sato Shunsuke, Suzuki Tomiko M., Jinnouchi Ryosuke, Ohba Nobuko, Asahi Ryoji, Morikawa Takeshi

JOURNAL OF PHYSICAL CHEMISTRY C Vol. 122 ( 4 ) page： 1921 - 1929 2018.2

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

Lee Joohwi, Ohba Nobuko, Asahi Ryoji

RSC ADVANCES Vol. 8 ( 45 ) page： 25534 - 25545 2018

Linear independent non-negative matrix factorization for automatic decomposition of experimental spectra Reviewed

KIKKAWA Nobuaki, SUZUMURA Akitoshi, TAJIMA Shin, YAMAKAWA Shunsuke, ASAHI Ryoji

Proceedings of the Annual Conference of JSAI Vol. 2018 ( 0 ) page： 3Z101 - 3Z101 2018

A universal 3D voxel descriptor for solid-state materials informatics with convolutional neural networks Reviewed

KAJITA Seiji, OHBA Nobuko, ASAHI Ryoji

Proceedings of the Annual Conference of JSAI Vol. 2018 ( 0 ) page： 3Z102 - 3Z102 2018

Authorship：Last author Language：Japanese Publishing type：Research paper (other academic) Publisher：The Japanese Society for Artificial Intelligence

<p>Materials informatics (MI) is a promising approach to liberate us from the time-consuming trial and error process for material discoveries. Contrary to molecular systems, however, practical successes of the solid-state MI are very scarce because existing descriptors insufficiently describe 3D features of eld quantities (e.g., electron distributions and local potentials). We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in such a suitable way to implement convolutional neural networks. We examine the reciprocal-lattice 3D voxel space descriptor encoded from the electron distribution by a regression task with 680 oxides data. The present scheme outperforms other descriptors in the prediction of Hartree energies that are signicantly relevant to the long-wavelength distribution of the valence electrons.</p>

DOI： 10.11517/pjsai.JSAI2018.0_3Z102

Visualization of SEM/EBSD Features of Ionic Conductivity with Convolutional Neural Networks Reviewed

KONDO Ruho, YAMAKAWA Shunsuke, MASUOKA Yumi, TAJIMA Shin, ASAHI Ryoji

Proceedings of the Annual Conference of JSAI Vol. 2018 ( 0 ) page： 3Z103 - 3Z103 2018

Authorship：Last author Language：Japanese Publishing type：Research paper (other academic) Publisher：The Japanese Society for Artificial Intelligence

<p>To automatically extract the features required to predict the material properties from microstructure, we utilized convolutional newral networks (CNNs). Training of CNNs was carried out by using experimentally obtained scanning electron microscope images and ionic conductivities. After training, the nodes, which placed after global average pooling (GAP), specically activated by passing through the images with high/low ionic conductivities were searched. Then, the corresponding feature maps just before GAP operations were shown. We found that the CNNs focused on the reagion containing large voids and on the area without any crystal defects for the specic features for the material with high ionic conductivities while on the small voids for low ionic conductivities. Such observations agree with knowledge of materials engineering.</p>

DOI： 10.11517/pjsai.JSAI2018.0_3Z103

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

Kajita Seiji, Ohba Nobuko, Jinnouchi Ryosuke, Asahi Ryoji

SCIENTIFIC REPORTS Vol. 7 ( 1 ) page： 16991 2017.12