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Shin Kiyohara

Showing results (1-10 of 13) with videos related to

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Microscopy (Oxford, England)|January 30, 2020
Machine learning approaches for ELNES/XANESTeruyasu Mizoguchi, Shin Kiyohara
The Journal of Chemical Physics|July 2, 2018
Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree searchShin Kiyohara, Teruyasu Mizoguchi
The Journal of Physical Chemistry Letters|May 19, 2025
Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material DesignsShin Kiyohara, Yu Kumagai
Science and Technology of Advanced Materials|September 4, 2024
First-principles calculations on dislocations in MgOShin Kiyohara, Tomohito Tsuru, Yu Kumagai
Journal of the American Chemical Society|March 28, 2024
Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer LearningShin Kiyohara, Yoyo Hinuma, Fumiyasu Oba
Ultramicroscopy|December 16, 2021
Automatic determination of the spectrum-structure relationship by tree structure-based unsupervised and supervised learningShin Kiyohara, Kakeru Kikumasa, Kiyou Shibata, et al.
Science Advances|February 1, 2017
Prediction of interface structures and energies via virtual screeningShin Kiyohara, Hiromi Oda, Tomohiro Miyata, et al.
Scientific Reports|September 8, 2018
Data-driven approach for the prediction and interpretation of core-electron loss spectroscopyShin Kiyohara, Tomohiro Miyata, Koji Tsuda, et al.
Scientific Data|May 16, 2022
Simulated carbon K edge spectral database of organic moleculesKiyou Shibata, Kakeru Kikumasa, Shin Kiyohara, et al.
Physical Review Letters|January 2, 2026
Machine-Learning Prediction of Charged-Defect Formation Energies from Crystal StructuresShin Kiyohara, Chisa Shibui, Soungmin Bae, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
Microscopy (Oxford, England)|January 30, 2020
Machine learning approaches for ELNES/XANESTeruyasu Mizoguchi, Shin Kiyohara
The Journal of Chemical Physics|July 2, 2018
Searching the stable segregation configuration at the grain boundary by a Monte Carlo tree searchShin Kiyohara, Teruyasu Mizoguchi
The Journal of Physical Chemistry Letters|May 19, 2025
Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material DesignsShin Kiyohara, Yu Kumagai
Science and Technology of Advanced Materials|September 4, 2024
First-principles calculations on dislocations in MgOShin Kiyohara, Tomohito Tsuru, Yu Kumagai
Journal of the American Chemical Society|March 28, 2024
Band Alignment of Oxides by Learnable Structural-Descriptor-Aided Neural Network and Transfer LearningShin Kiyohara, Yoyo Hinuma, Fumiyasu Oba
Ultramicroscopy|December 16, 2021
Automatic determination of the spectrum-structure relationship by tree structure-based unsupervised and supervised learningShin Kiyohara, Kakeru Kikumasa, Kiyou Shibata, et al.
Science Advances|February 1, 2017
Prediction of interface structures and energies via virtual screeningShin Kiyohara, Hiromi Oda, Tomohiro Miyata, et al.
Scientific Reports|September 8, 2018
Data-driven approach for the prediction and interpretation of core-electron loss spectroscopyShin Kiyohara, Tomohiro Miyata, Koji Tsuda, et al.
Scientific Data|May 16, 2022
Simulated carbon K edge spectral database of organic moleculesKiyou Shibata, Kakeru Kikumasa, Shin Kiyohara, et al.
Physical Review Letters|January 2, 2026
Machine-Learning Prediction of Charged-Defect Formation Energies from Crystal StructuresShin Kiyohara, Chisa Shibui, Soungmin Bae, et al.
Pageof 2