[18a-A21-1~11] 23.1 Joint Session N "Informatics" | The 85th JSAP Autumn Meeting 2024

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Session Details

Oral presentation23 Joint Session N "Informatics" : 23.1 Joint Session N "Informatics"

[18a-A21-1~11]23.1 Joint Session N "Informatics"

Wed. Sep 18, 2024 9:00 AM - 12:00 PM JST
Wed. Sep 18, 2024 12:00 AM - 3:00 AM UTC

A21 (TOKI MESSE 2F)

Kiyou Shibata(the University of Tokyo), Takuya Shibayama(Preferred Networks)

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9:00 AM - 9:15 AM JST(12:00 AM - 12:15 AM UTC)

[18a-A21-1]Verification of Material Exploration Method Utilizing Quantum-Inspired Technologies

〇Kazuhiro Hashiguchi¹, Akito Maruo¹, Shinji Iwane¹, Hideyuki Jippo¹, Yoshinori Suga² (1.Fujitsu Ltd., 2.Toyota Motor Corp.)

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9:15 AM - 9:30 AM JST(12:15 AM - 12:30 AM UTC)

[18a-A21-2]Modeling and Optimization of Materials Property in Binary Latent Space

〇Masahiko Ishida¹ (1.NEC Corp.)

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9:30 AM - 9:45 AM JST(12:30 AM - 12:45 AM UTC)

[18a-A21-3]Geometric concept learning of crystal structures

〇Keisuke Ozawa¹, Teppei Suzuki¹, Shunsuke Tonogai², Tomoya Itakura² (1.DENSO IT Lab., 2.DENSO CORP.)

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9:45 AM - 10:00 AM JST(12:45 AM - 1:00 AM UTC)

[18a-A21-4]Crystal structure generation based on polyhedra by graph theory

〇Tomoyasu Yokoyama¹, Kazuhide Ichikawa¹, Hisashi Naito² (1.Panasonic Holdings Corp., 2.Nagoya Univ.)

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10:00 AM - 10:15 AM JST(1:00 AM - 1:15 AM UTC)

[18a-A21-5]Study of a Graph Neural Network Using Isolated Atom Electronic Structures as Descriptors

〇Kiyou Shibata¹, Teruyasu Mizoguchi¹ (1.The Univ. of Tokyo)

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10:15 AM - 10:30 AM JST(1:15 AM - 1:30 AM UTC)

[18a-A21-6]Global mapping of thermoelectric material properties using crystal graph

〇Yusuke Hashimoto¹, Xue Jia², Hao Li², Takaaki Tomai¹ (1.FRIS Tohoku Univ., 2.AIMR Tohoku Univ.)

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10:45 AM - 11:00 AM JST(1:45 AM - 2:00 AM UTC)

[18a-A21-7]Investigation of Training Dataset for Reproducing Ni/Ge interfaces
in a Graph Neural Network Potential

〇(M1)Machika Naito¹, Yusuke Nishimura¹, Takanobu Watanabe¹ (1.Waseda Univ.)

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11:00 AM - 11:15 AM JST(2:00 AM - 2:15 AM UTC)

[18a-A21-8]Investigation of effective dataset required to construct graph neural network potential for SiO₂/Si interface

〇(M1)Kotaro Takematsu¹, Kentaro Hirai¹, Yusuke Nishimura¹, Takanobu Watanabe¹ (1.Waseda Univ.)

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11:15 AM - 11:30 AM JST(2:15 AM - 2:30 AM UTC)

[18a-A21-9]Thermal Boundary Resistance between Interconnect Unitary Metals for Beyond 2nm Logic Nodes and SiO₂ Dielectric: Molecular Dynamics Calculation based on Neural Network Potential

〇Shuichiro Hashimoto¹, Yusuke Nishimura², Takanobu Watanabe^1,2 (1.Wasda Univ. SEES, 2.Waseda Univ. FSE)

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11:30 AM - 11:45 AM JST(2:30 AM - 2:45 AM UTC)

[18a-A21-10]Crystal Structure Prediction Using Universal Neural Network Potential PFP

〇Takuya Shibayama¹, Kohei Shinohara¹, Hideaki Imamura¹, Katsuhiko Nishimra¹, So Takamoto¹, Chikashi Shinagawa¹ (1.PFN)

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11:45 AM - 12:00 PM JST(2:45 AM - 3:00 AM UTC)

[18a-A21-11]Support for all stable elements and improved robustness of universal neural network potential PFP

〇So Takamoto¹, Chikashi Shinagawa¹ (1.PFN)

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Session Details

[18a-A21-1~11]23.1 Joint Session N "Informatics"

[18a-A21-1]Verification of Material Exploration Method Utilizing Quantum-Inspired Technologies

[18a-A21-2]Modeling and Optimization of Materials Property in Binary Latent Space

[18a-A21-3]Geometric concept learning of crystal structures

[18a-A21-4]Crystal structure generation based on polyhedra by graph theory

[18a-A21-5]Study of a Graph Neural Network Using Isolated Atom Electronic Structures as Descriptors

[18a-A21-6]Global mapping of thermoelectric material properties using crystal graph

[18a-A21-7]Investigation of Training Dataset for Reproducing Ni/Ge interfaces in a Graph Neural Network Potential

[18a-A21-8]Investigation of effective dataset required to construct graph neural network potential for SiO2/Si interface

[18a-A21-9]Thermal Boundary Resistance between Interconnect Unitary Metals for Beyond 2nm Logic Nodes and SiO2 Dielectric: Molecular Dynamics Calculation based on Neural Network Potential

[18a-A21-10]Crystal Structure Prediction Using Universal Neural Network Potential PFP

[18a-A21-11]Support for all stable elements and improved robustness of universal neural network potential PFP

[18a-A21-7]Investigation of Training Dataset for Reproducing Ni/Ge interfaces
in a Graph Neural Network Potential

[18a-A21-8]Investigation of effective dataset required to construct graph neural network potential for SiO₂/Si interface

[18a-A21-9]Thermal Boundary Resistance between Interconnect Unitary Metals for Beyond 2nm Logic Nodes and SiO₂ Dielectric: Molecular Dynamics Calculation based on Neural Network Potential