Index Catalog // MDR

Description/Abstract:: Understanding disordered structure is difficult due to insufficient information in experimental data. Here we overcome this issue by usin...
Keyword:: Glass, Neutron diffraction, Structure, and X-ray diffraction
Resource Type:: Article
Author:: Yuta Shuseki, Shinji Kohara, Tomoaki Kaneko, Keitaro Sodeyama, Yohei Onodera, Chihiro Koyama, Atsunobu Masuno, Shunta Sasaki, Shohei Hatano, Motoki Shiga, Ippei Obayashi, Yasuaki Hiraoka, Junpei T. Okada, Akitoshi Mizuno, Yuki Watanabe, Yui Nakata, Koji Ohara, Motohiko Murakami, Matthew G. Tucker, Marshall T. McDonnell, Hirohisa Oda, and Takehiko Ishikawa
Journal:: The Journal of Physical Chemistry A
Date Uploaded:: 01/04/2024

Description/Abstract:: MgM2O4 (M = Mn, Fe, Co) spinels, which transform into rock-salt phases on Mg incorporation, are attractive cathode materials for future M...
Keyword:: Cathode, First-principles calculations, Mg-battery, Spinel Surface, and Surface reconstruction
Resource Type:: Article
Author:: Tomoaki Kaneko, Yui Fujihara, Hiroaki Kobayashi, and Keitaro Sodeyama
Date Uploaded:: 01/02/2023
Date Modified:: 20/03/2024

Description/Abstract:: We combined a data science-driven method with quantum chemistry calculations, and applied it to the battery electrolyte problem. We perfo...
Keyword:: electrolyte, battery, and quantum chemistry
Resource Type:: Dataset
Data origin:: simulations
Author:: ISHIKAWA, Atsushi, SODEYAMA, Keitaro, IGARASHI, Yasuhiko, NAKAYAMA, Tomofumi, TATEYAMA, Yoshitaka, and OKADA, Masato
Journal:: Physical Chemistry Chemical Physics
Date Uploaded:: 24/09/2021
Date Modified:: 01/10/2021

Description/Abstract:: We implemented an active learning pipeline assisted by machine learning and Bayesian optimization (ALMLBO) for predicting magnetic proper...
Keyword:: Active learning, Machine learning, Bayesian Optimization, Nd-Fe-B magnet, Hot extrusion, Process, Coercivity, Remanence, Squareness, and Maximum energy product
Material/Specimen:: Anisotropic permanent magnets fabricated by hot extrusion using a commercial Nd14Fe76Co3.4B6Ga0.6 (at%) powder (MQU-F™)
Resource Type:: Dataset
Data origin:: experiments
Author:: Lambard, Guillaume, Sasaki, Taisuke, Sodeyama, Keitaro, Ohkubo, Tadakatsu, and Hono, Kazuhiro
Operator:: PRYTULIAK, Anastasiia and TOYOOKA, Yoshiya
Date Uploaded:: 11/05/2021
Date Modified:: 11/10/2021

Description/Abstract:: Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental d...
Keyword:: Machine learning, active learning, and adhesive
Resource Type:: Article
Author:: Pruksawan, Sirawit, Lambard, Guillaume, Samitsu, Sadaki, Sodeyama, Keitaro, and Naito, Masanobu
Journal:: Science and Technology of Advanced Materials
Date Uploaded:: 02/01/2021
Date Modified:: 18/10/2022

Description/Abstract:: Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast...
Keyword:: Gaussian09, Li-ion battery, materials informatics, molecules, organic solvents, and quantum chemistry calculations
Material/Specimen:: organic solvents
Resource Type:: Dataset
Data origin:: simulations
Author:: SODEYAMA, Keitaro, IGARASHI, Yasuhiko, NAKAYAMA, Tomofumi, TATEYAMA, Yoshitaka, and OKADA, Masato
Journal:: Physical Chemistry Chemical Physics
Date Uploaded:: 09/09/2020
Date Modified:: 30/06/2021

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