Zobrazeno 1 - 10
of 336
pro vyhledávání: '"Kenichi Oyaizu"'
Publikováno v:
ACS Omega, Vol 9, Iss 1, Pp 1463-1471 (2023)
Externí odkaz:
https://doaj.org/article/eb45ddb385cf4ad4a34aec583f90ea34
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-10 (2023)
Abstract Redox targeting reaction is an emerging idea for boosting the energy density of redox-flow batteries: mobile redox mediators transport electrical charges in the cells, whereas large-density electrode-active materials are fixed in tanks. This
Externí odkaz:
https://doaj.org/article/9db4df0ebab54143991d99b3033d0cd9
Autor:
Kan Hatakeyama-Sato, Momoka Umeki, Hiroki Adachi, Naoaki Kuwata, Gen Hasegawa, Kenichi Oyaizu
Publikováno v:
npj Computational Materials, Vol 8, Iss 1, Pp 1-8 (2022)
Abstract Data-driven material exploration is a ground-breaking research style; however, daily experimental results are difficult to record, analyze, and share. We report a data platform that losslessly describes the relationships of structures, prope
Externí odkaz:
https://doaj.org/article/94eefd0cdfbe4cb8ab872ad583d3228c
Autor:
Kan Hatakeyama-Sato, Kenichi Oyaizu
Publikováno v:
ACS Omega, Vol 6, Iss 22, Pp 14566-14574 (2021)
Externí odkaz:
https://doaj.org/article/8af2313846f64b988dbea65766c67902
Autor:
Kan Hatakeyama-Sato, Kenichi Oyaizu
Publikováno v:
Communications Materials, Vol 1, Iss 1, Pp 1-10 (2020)
Traditionally, machine learning for materials science is based on database-specific models and is limited in the number of predictable parameters. Here, a versatile graph-based neural network can integrate multiple data sources, allowing the predicti
Externí odkaz:
https://doaj.org/article/df9217b218ca4e14b7b2a461db83f7d6
Publikováno v:
Bulletin of the Chemical Society of Japan; May2024, Vol. 97 Issue 5, p1-8, 8p
Publikováno v:
ACS Applied Polymer Materials. 5:2307-2311
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b2ef4e9e95bb2f3e6342a31abe60fd0a
https://doi.org/10.1021/acsapm.3c00391
https://doi.org/10.1021/acsapm.3c00391
Publikováno v:
Advanced Intelligent Systems, Vol 3, Iss 4, Pp n/a-n/a (2021)
Efficient screening of chemicals is essential for exploring new materials. However, the search space is astronomically large, making calculations with conventional computers infeasible. For example, an N‐component system of organic molecules genera
Externí odkaz:
https://doaj.org/article/a2eee5898b6b441193a2224d4615fb07
Publikováno v:
RSC Advances. 13:547-557
Charge-transport kinetics of redox-active polymers is essential in designing electrochemical devices. We formulate the homogeneous and heterogeneous charge-transfer processes of the redox-active polymers dissolved in electrolytes. The critical electr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1efcf6c701eaa7e2afe71f594d4aae6c
https://doi.org/10.1039/d2ra07208d
https://doi.org/10.1039/d2ra07208d
Publikováno v:
RSC Advances. 13:14651-14659
A quantum-inspired annealing system with a hybrid algorithm accelerates functional material discovery, shown by high-conductivity polymer electrolytes.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7b3a9b0e5f4eb3ac72fb823adbb23f9
https://doi.org/10.1039/d3ra01982a
https://doi.org/10.1039/d3ra01982a