Oral presentation

[11a-N107-1~11] 23.1 Joint Session N "Informatics"

Sat. Sep 11, 2021 9:00 AM - 12:00 PM N107 (Oral)

Kentaro Kutsukake(RIKEN), Yukari Katsura(NIMS)

△：Presentation by Applicant for JSAP Young Scientists Presentation Award
▲：English Presentation
▼：Both of Above
No Mark：None of Above

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9:00 AM - 9:15 AM

[11a-N107-1] [Young Scientist Presentation Award Speech] Classification of magnetization reversal process and extraction of hidden parameter using persistent homology

〇Sotaro Kunii¹, Alexandre Foggiatto¹, Chiharu Mitsumata², Masato Kotsugi¹ (1.Tokyo Univ. of Science, 2.NIMS)

9:15 AM - 9:30 AM

[11a-N107-2] Analysis of the coercivity mechanism of labyrinth magnetic domain based on the extended Landau free energy model

〇Ken Masuzawa¹, Sotaro Kunii¹, Alexandre Foggiatto¹, Chiharu Mitsumata², Masato Kotsugi¹ (1.TUS, 2.NIMS)

9:30 AM - 9:45 AM

[11a-N107-3] First principles calculations and machine learning for dielectric properties in strong correlated materials

〇(M2)Takahiro Matsumoto¹, Kenji Nawa¹, Koji Nakamura¹ (1.Mie Univ.)

9:45 AM - 10:00 AM

[11a-N107-4] Optimization of measurement process for magnetocaloric materials by neural network

〇Kensei Terashima¹, Pedro Baptista de Castro^1,2, Takafumi Yamamoto D¹, Miren Garvine Esparza Eshevarria^1,2, Ryo Matsumoto¹, Hiroyuki Takeya¹, Yoshihiko Takano^1,2 (1.NIMS, 2.Univ. of Tsukuba)

10:00 AM - 10:15 AM

▼ [11a-N107-5] Quantitative relationship between 3D structure-ionic conductivity by topological analysis

〇(D)YU WEN^1,2, AYAKO HASHIMOTO^1,2,3, AKIHIKO HIRATA⁴, HIDEKI ABE^1,3,5 (1.NIMS, 2.Univ. of Tsukuba, 3.JST, 4.Waseda Univ., 5.Saitama Univ.)

10:30 AM - 10:45 AM

[11a-N107-6] Extraction of crystal structure from database using the dissimilarity of local structures

〇Shota Takemura¹, Takashi Takeda¹, Takayuki Nakanishi¹, Yukinori Koyama¹, Hidekazu Ikeno², Naoto Hirosaki¹ (1.NIMS, 2.Osaka Prefecture Univ.)

10:45 AM - 11:00 AM

△ [11a-N107-7] High-precision clustering using molecular similarity evaluation with Digital Annealer

〇Akito Maruo^1,2, Hideyuki Jippo¹ (1.Fujitsu, 2.Hokkaido Univ.)

11:00 AM - 11:15 AM

▲ [11a-N107-8] Efficient workflow for automatic database creation from large scale scientific articles

〇Luca Foppiano¹, Pedro Baptista De Castro², Kensei Terashima², Yoshihiko Takano², Masashi Ishii¹ (1.MaDIS, NIMS, 2.MANA, NIMS)

11:15 AM - 11:30 AM

[11a-N107-9] Automatic data extraction from scientific articles using the materials-dictionary database:Improvement of accuracy for obtaining physical properties

〇Akira Suzuki¹, Masashi Ishii¹ (1.NIMS)

11:30 AM - 11:45 AM

[11a-N107-10] Knowledge base construction for MI using PoLyInfo RDF

Koichi Sakamoto¹, 〇Masashi Ishii¹ (1.NIMS)

11:45 AM - 12:00 PM

△ [11a-N107-11] [Highlight]Deep Learning Virtual Experimentations for Materials and Process Informatics of Tangible Materials

〇Shun Muroga¹, Takashi Honda², Hideaki Nakajima¹, Kazufumi Kobashi¹, Taiyo Shimizu¹, Hiroshi Morita¹, Toshiya Okazaki¹, Kenji Hata¹ (1.AIST, 2.ADMAT)

Oral presentation

[11p-N107-1~16] 23.1 Joint Session N "Informatics"

Sat. Sep 11, 2021 1:30 PM - 6:00 PM N107 (Oral)

Kenji Tsujino(Tokyo women's medical Univ.), Takuto Kojima(Nagoya Univ.), Yukinori Koyama(NIMS)

△：Presentation by Applicant for JSAP Young Scientists Presentation Award
▲：English Presentation
▼：Both of Above
No Mark：None of Above

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1:30 PM - 1:45 PM

[11p-N107-1] Massively parallel Monte Carlo calculation in advanced measurement data analysis

〇Takeo Hoshi^1,2, Harumichi Iwamoto¹, Koji Hukushima³ (1.Tottori Univ., 2.KEK-IMSS-SPF, 3.Univ. Tokyo)

1:45 PM - 2:00 PM

[11p-N107-2] Ramer–Douglas–Peucker Algorithm for quantum computer

〇Yuichiro Minato¹, Takao Tomono² (1.blueqat Inc., 2.DI Div., TOPPAN)

2:00 PM - 2:15 PM

△ [11p-N107-3] Deep Learning Estimation of Impurity Effects in Quantum Wire

〇Youichi Kodama¹, Shin Hasegawa¹, Masami Noginuma¹, Koujin Takeda¹, Tomosuke Aono¹ (1.Ibaraki Univ.)

2:15 PM - 2:30 PM

[11p-N107-4] Si photonic crystal slow-light waveguide optimized by informatics technology

〇Keisuke Hirotani¹, Ryo Shiratori¹, Toshihiko Baba¹ (1.Yokohama Nat'l Univ.)

2:30 PM - 2:45 PM

[11p-N107-5] Structural Optimization of Si Photonic Crystal Waveguide Beam Steering Device Using Covariance Matrix Adaptation Evolution Strategy

〇Saneyuki Suyama¹, Ryo Shiratori¹, Toshihiko Baba¹ (1.Yokohama Nat'l Univ.)

3:00 PM - 3:15 PM

[11p-N107-6] Crystal Structure Prediction by Bayesian Optimization and Evolutionary algorithm

〇Tomoki Yamashita¹, Hiori Kino², Koji Tsuda^2,3,4, Takashi Miyake⁵, Tamio Oguchi⁶ (1.Nagaoka Univ. Tech., 2.NIMS, 3.Univ. Tokyo, 4.RIKEN, 5.AIST, 6.Osaka Univ.)

3:15 PM - 3:30 PM

[11p-N107-7] Prediction and Fabrication of Large Magnetic Anisotropy Materials by Bayesian Optimization

〇(M1)Daigo Furuya¹, Taichi Nakao¹, Hyuga Saito¹, Koichi Uda¹, Yoshio Miura², Masato Kotsugi¹ (1.Tokyo Univ.of Science, 2.NIMS)

3:30 PM - 3:45 PM

▲ [11p-N107-8] Search for suitable hyperparameters in Bayesian optimization for material synthesis: three-dimensional case

〇(D)Han XU¹, Takefumi Kimura¹, Ryo Nakayama¹, Ryota Shimizu^1,2, Yasunobu Ando³, Nobuaki Yasuo¹, Masakazu Sekijima¹, Taro Hitosugi¹ (1.Tokyo Tech, 2.JST-PRESTO, 3.AIST)

3:45 PM - 4:00 PM

[11p-N107-9] Heat source layout optimization to lower the maximum temperature for electronic substrate

〇(M1)Rikuya Sato¹, Kentaro Kutsukake², Shunta Harada^1,3, Miho Tagawa^1,3, Toru Ujihara^1,3 (1.Grad.School of Eng.Nagoya Univ., 2.AIP RIKEN, 3.IMass Nagoya Univ.)

4:00 PM - 4:15 PM

[11p-N107-10] Optimization of flow distribution in SiC solution growth using topology and machine learning

〇(M2)Masaru Isono¹, Tomoo Yokoyama², Kentaro Kutsukake^3,4, Shunta Harada^1,4, Miho Tagawa^1,4, Toru Ujihara^1,4 (1.Grad. School of Eng. Nagoya Univ., 2.Dep. of Eng. Gifu Univ., 3.AIP RIKEN, 4.IMaSS Nagoya Univ.)

4:15 PM - 4:30 PM

△ [11p-N107-11] Estimation of melt state dynamics in floating zone method using Gaussian mixture model

〇Ryo Omae¹, Shogo Sumitani¹, Yusuke Tosa¹, Shunta Harada² (1.Anamorphosis Networks, 2.Nagoya Univ.)

4:45 PM - 5:00 PM

[11p-N107-12] Development of an autonomous X-ray diffractometer

〇(M1)Yusaku Nakajima^1,2, Takafumi Hawai¹, Kanta Ono^1,2,3 (1.KEK, 2.SOKENDAI, 3.Osaka Univ.)

5:00 PM - 5:15 PM

△ [11p-N107-13] Automated K-edge XANES spectrum separation

〇Kazuma Yanai¹, Nanase Moriguchi¹, Hitoshi Yamaguchi¹, Taisuke Ono¹ (1.DENSO CORP.)

5:15 PM - 5:30 PM

[11p-N107-14] Twinning network graph analysis on ingot-scale structure of multicrystalline materials

〇Takuto Kojima¹, Kentaro Kutsukake², Tetsuya Matsumoto¹, Hiroaki Kudo¹, Noritaka Usami³ (1.Grad. Info. Nagoya Univ., 2.RIKEN AIP, 3.Grad. Eng. Nagoya Univ.)

5:30 PM - 5:45 PM

△ [11p-N107-15] Training data optimization and error analysis
for machine learning-based crystal orientation estimation

〇(M1)Kyoka Hara¹, Takuto Kojima², Kentaro Kutsukake³, Hiroaki Kudo², Noritaka Usami¹ (1.Grad. Eng. Nagoya Univ., 2.Grad. Info. Nagoya Univ., 3.Riken AIP)

5:45 PM - 6:00 PM

[11p-N107-16] Prediction of carrier recombination velocity of grain boundaries from photoluminescence intensity profile using machine learning

〇Kentaro Kutsukake¹, Kazuki Mitamura², Takuto Kojima³, Noritaka Usami² (1.AIP RIKEN, 2.Grad. Eng. Nagoya Univ., 3.Grad. Info. Nagoya Univ.)

Sessions

23 Joint Session N "Informatics" » 23.1 Joint Session N "Informatics"

[11a-N107-1~11] 23.1 Joint Session N "Informatics"

[11a-N107-1] [Young Scientist Presentation Award Speech] Classification of magnetization reversal process and extraction of hidden parameter using persistent homology

[11a-N107-2] Analysis of the coercivity mechanism of labyrinth magnetic domain based on the extended Landau free energy model

[11a-N107-3] First principles calculations and machine learning for dielectric properties in strong correlated materials

[11a-N107-4] Optimization of measurement process for magnetocaloric materials by neural network

▼ [11a-N107-5] Quantitative relationship between 3D structure-ionic conductivity by topological analysis

[11a-N107-6] Extraction of crystal structure from database using the dissimilarity of local structures

△ [11a-N107-7] High-precision clustering using molecular similarity evaluation with Digital Annealer

▲ [11a-N107-8] Efficient workflow for automatic database creation from large scale scientific articles

[11a-N107-9] Automatic data extraction from scientific articles using the materials-dictionary database:Improvement of accuracy for obtaining physical properties

[11a-N107-10] Knowledge base construction for MI using PoLyInfo RDF

△ [11a-N107-11] [Highlight]Deep Learning Virtual Experimentations for Materials and Process Informatics of Tangible Materials

[11p-N107-1~16] 23.1 Joint Session N "Informatics"

[11p-N107-1] Massively parallel Monte Carlo calculation in advanced measurement data analysis

[11p-N107-2] Ramer–Douglas–Peucker Algorithm for quantum computer

△ [11p-N107-3] Deep Learning Estimation of Impurity Effects in Quantum Wire

[11p-N107-4] Si photonic crystal slow-light waveguide optimized by informatics technology

[11p-N107-5] Structural Optimization of Si Photonic Crystal Waveguide Beam Steering Device Using Covariance Matrix Adaptation Evolution Strategy

[11p-N107-6] Crystal Structure Prediction by Bayesian Optimization and Evolutionary algorithm

[11p-N107-7] Prediction and Fabrication of Large Magnetic Anisotropy Materials by Bayesian Optimization

▲ [11p-N107-8] Search for suitable hyperparameters in Bayesian optimization for material synthesis: three-dimensional case

[11p-N107-9] Heat source layout optimization to lower the maximum temperature for electronic substrate

[11p-N107-10] Optimization of flow distribution in SiC solution growth using topology and machine learning

△ [11p-N107-11] Estimation of melt state dynamics in floating zone method using Gaussian mixture model

[11p-N107-12] Development of an autonomous X-ray diffractometer

△ [11p-N107-13] Automated K-edge XANES spectrum separation

[11p-N107-14] Twinning network graph analysis on ingot-scale structure of multicrystalline materials

△ [11p-N107-15] Training data optimization and error analysis
for machine learning-based crystal orientation estimation

[11p-N107-16] Prediction of carrier recombination velocity of grain boundaries from photoluminescence intensity profile using machine learning

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Sessions

23 Joint Session N "Informatics" » 23.1 Joint Session N "Informatics"

[11a-N107-1~11] 23.1 Joint Session N "Informatics"

▼ [11a-N107-5] Quantitative relationship between 3D structure-ionic conductivity by topological analysis

△ [11a-N107-7] High-precision clustering using molecular similarity evaluation with Digital Annealer

▲ [11a-N107-8] Efficient workflow for automatic database creation from large scale scientific articles

△ [11a-N107-11] [Highlight]Deep Learning Virtual Experimentations for Materials and Process Informatics of Tangible Materials

[11p-N107-1~16] 23.1 Joint Session N "Informatics"

△ [11p-N107-3] Deep Learning Estimation of Impurity Effects in Quantum Wire

▲ [11p-N107-8] Search for suitable hyperparameters in Bayesian optimization for material synthesis: three-dimensional case

△ [11p-N107-11] Estimation of melt state dynamics in floating zone method using Gaussian mixture model

△ [11p-N107-13] Automated K-edge XANES spectrum separation

△ [11p-N107-15] Training data optimization and error analysisfor machine learning-based crystal orientation estimation

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△ [11p-N107-15] Training data optimization and error analysis
for machine learning-based crystal orientation estimation