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Your search keyword '"Miyazato, Itsuki"' showing total 35 results
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35 results on '"Miyazato, Itsuki"'

1. The Rising Sun Envelope Method: an automatic and accurate peak location technique for XANES measurements

Author

Monteiro, Rafael, Miyazato, Itsuki, and Takahashi, Keisuke

Subjects
Condensed Matter - Materials Science, Electrical Engineering and Systems Science - Signal Processing, Physics - Chemical Physics
Abstract

The lack of theoretical understanding of X-Ray Absorption Near Edge Structure (XANES) spectroscopy makes the development of analysis tools for its study a necessity. Here, an algorithm for judicious choice of local minima and maxima points of XANES spectrum (experimental or simulated) is proposed, without any loss of information on peaks location nor on peak strength. We call it the Rising Sun Envelope Method, since it is based on successive regularizations of the spectral measurement that, according to parameter choices that are intrinsic to the measurements, keep peaks location and strength as invariants. This is the first method that finds peaks in XANES automatically, without depending on first derivative information. Nevertheless, a direct computation of Absorption-Edge is provided, where we avoid the issue inflection point computations based on the XANES second derivative, dealing instead with simpler computations of inflection points of higher quality cubic spline approximation. Besides applications of the algorithm to XANES, we illustrate further applications in Electron Energy Loss Spectroscopy (EELS) and Raman spectra., Comment: 10 pages, 12 figures

Published
2019
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23. The Rise of Catalyst Informatics: Towards Catalyst Genomics

Author

Takahashi, Keisuke, Takahashi, Lauren, Miyazato, Itsuki, Fujima, Jun, Tanaka, Yuzuru, Uno, Takeaki, Satoh, Hiroko, Ohno, Koichi, Nishida, Mayumi, Hirai, Kenji, Ohyama, Junya, Nguyen, Thanh Nhat, Nishimura, Shun, Taniike, Toshiaki, University of Zurich, and Takahashi, Keisuke

Subjects
10120 Department of Chemistry, Inorganic Chemistry, 1503 Catalysis, 1604 Inorganic Chemistry, 540 Chemistry, Organic Chemistry, Physical and Theoretical Chemistry, 1606 Physical and Theoretical Chemistry, Catalysis, 1605 Organic Chemistry
Published
2019

24. The Rise of Catalyst Informatics: Towards Catalyst Genomics

Author

Takahashi, Keisuke, primary, Takahashi, Lauren, additional, Miyazato, Itsuki, additional, Fujima, Jun, additional, Tanaka, Yuzuru, additional, Uno, Takeaki, additional, Satoh, Hiroko, additional, Ohno, Koichi, additional, Nishida, Mayumi, additional, Hirai, Kenji, additional, Ohyama, Junya, additional, Nguyen, Thanh Nhat, additional, Nishimura, Shun, additional, and Taniike, Toshiaki, additional

Published
2019
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27. Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning.

Author

Sugiyama, Kanami, Nguyen, Thanh Nhat, Nakanowatari, Sunao, Miyazato, Itsuki, Taniike, Toshiaki, and Takahashi, Keisuke

Subjects
OXIDATIVE coupling, DEEP learning, CATALYSTS, HETEROGENEOUS catalysts, METHANE
Abstract

The combination of deep learning and high‐throughput experiments is proposed for the direct design of heterogeneous catalysts in the oxidative coupling of methane (OCM) reaction. Deep learning predicts 20 active catalysts from high‐throughput 12,708 OCM experimental data where 19 of the predicted 20 catalysts have not been previously reported. The predicted 20 catalysts are then evaluated through high‐throughput experiments where a highly active unreported catalyst Ti−Na2WO4/TiO2 is discovered. Ti−Na2WO4/TiO2 results in a high C2 yield of 18.8 % where the maximum C2 yield is reported to be approximately 20 % within the 12,708 OCM data. Furthermore, the experimental conditions predicted for Ti−Na2WO4/TiO2 are also reproduced by high‐throughput experiment. Thus, deep learning demonstrates that both catalysts and experimental conditions can be simultaneously explored for designing catalysts. More importantly, deep learning assisted catalysts search is found to dramatically increase the chances of finding active catalysts where 9 out of 20 predicted catalysts result in a C2 yield over 15 %. Therefore, the combination of deep learning with high‐throughput experiments is proposed to be an effective strategy for the direct design of catalysts. [ABSTRACT FROM AUTHOR]

Published
2021
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28. Controlling electronic structure of single-layered HfX3 (X=S, Se) trichalcogenides through systematic Zr doping.

Author

Miyazato, Itsuki, Sarikurt, Sevil, Takahashi, Keisuke, and Ersan, Fatih

Subjects
ELECTRONIC structure, REDUCTION potential, ELECTRONIC control, SEMIMETALS, DOPING in sports, ATOMS
Abstract

The electronic structures of Hf 1 - x Zr x S 3 and Hf 1 - x Zr x Se 3 trichalcogenides are investigated by first-principles calculation. In particular, step change of Zr concentration is intensively investigated. Our calculations reveal that doping of Zr atoms increase the strength of cohesion between the atoms in HfX 3 (X = S, Se) monolayers, and results in occurring of energetically more stable alloys. In addition, doping of Zr atoms in HfS 3 causes band gap bowing, which means the curve of band gap values shows quadratic nonlinearities while change from semimetal to semiconductor is observed in HfSe 3 case. The examined band structures indicate that Hf 1 - x Zr x S 3 monolayers have very suitable band gap values for water splitting and also their band edge potentials have sufficiently higher or lower positions than the required potential values for the reduction or oxidation potentials. [ABSTRACT FROM AUTHOR]

Published
2020
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34. Direct design of active catalysts for low temperature oxidative coupling of methane viamachine learning and data miningElectronic supplementary information (ESI) available: Reaction results. See DOI: 10.1039/d0cy01751e

Author

Ohyama, Junya, Kinoshita, Takaaki, Funada, Eri, Yoshida, Hiroshi, Machida, Masato, Nishimura, Shun, Uno, Takeaki, Fujima, Jun, Miyazato, Itsuki, Takahashi, Lauren, and Takahashi, Keisuke

Abstract

Direct design of low temperature oxidative coupling of methane (OCM) catalysts is proposed viamachine learning and data mining. 58 OCM catalysts are experimentally synthesized and evaluated. The collected 58 sets of data are then classified by unsupervised machine learning in a multi-dimensional space where an active catalyst group for low temperature OCM is identified. Data mining then identifies the physical rule within the group. Catalysts satisfying such a physical rule are designed where 2 undiscovered low temperature OCM catalysts are found and experimentally validated. Thus, machine learning and data mining reveal the hidden physical rule behind the catalysis leading to the direct design of catalysts. Hence, machine learning and data mining open up the insight on a powerful strategy for designing catalysts.

Published
2021
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