[P] P107~P114 | 日本金属学会2024年秋期(第175回)講演大会

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Session Details

ポスターセッション11.Computational Science : Computational Science

[P]P107~P114

Wed. Sep 18, 2024 2:00 PM - 3:30 PM JST
Wed. Sep 18, 2024 5:00 AM - 6:30 AM UTC

Poster Session Room1 Assembly Hall at Osaka Univ. Hall

2:00 PM - 3:30 PM JST(5:00 AM - 6:30 AM UTC)

[P107]Development of a bidirectional PSP linkage model using deep generative models

*Nakagami Rika¹, Inoue Junya², Noguchi Satoshi³ (1. Eng UTokyo, 2. IIS UTokyo, 3. JAMSTEC)

2:00 PM - 3:30 PM JST(5:00 AM - 6:30 AM UTC)

[P108]Prediction of Physical Properties of Garnet-Type Structured Compounds by Machine Learning

*Haruki HIRAISHI¹, Tomoyuki Yamamoto³, Hai　Chau Nguyen² (1. Waseda University, 2. VNU-UET, 3. Waseda University)

2:00 PM - 3:30 PM JST(5:00 AM - 6:30 AM UTC)

[P109]Oxygen incorporation in MAX phases: Insights into the structures and elastic properties of the Ti-Al-C-O and Ti-Al-N-O system

*Hyokyeong Kim¹, Junyoung Choi¹, Inseong Bae², Jiwoo Choi¹, Jiwoong Kim^1,2 (1. Department of Materials Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea, 2. Department of Green Chemistry and Materials Engineering, Soongsil University, Seoul 06978, Republic of Korea)

2:00 PM - 3:30 PM JST(5:00 AM - 6:30 AM UTC)

[P110]Predicting Thermal Expansion Properties of Complex Composite Materials Using Multi-Scale Simulation

*Jinyong Lee¹, Hyokyeong Kim¹, Sooah Kyung¹, Jongwook Kwak¹, Jiwoong Kim^1,2 (1. Department of Materials Science and Engineering, Soongsil University, Seoul 06978, Republic of Korea, 2. Department of Green Chemistry and Materials Engineering, Soongsil University, Seoul 06978, Republic of Korea)

2:00 PM - 3:30 PM JST(5:00 AM - 6:30 AM UTC)

日本金属学会2024年秋期(第175回)講演大会

Session Details

[P]P107~P114

[P107]Development of a bidirectional PSP linkage model using deep generative models

[P108]Prediction of Physical Properties of Garnet-Type Structured Compounds by Machine Learning

[P109]Oxygen incorporation in MAX phases: Insights into the structures and elastic properties of the Ti-Al-C-O and Ti-Al-N-O system

[P110]Predicting Thermal Expansion Properties of Complex Composite Materials Using Multi-Scale Simulation

[P111]Prediction Model for Physical Properties of High-Entropy Alloys Using Convolutional Neural Networks

[P112]Effects of Thermal Cycling on Microscopic Structures of Fe–B–Si Amorphous Alloys:
A Molecular Dynamics Study

[P113]Molecular dynamics analysis of the effect of carbon atoms on the solid phase transformation of steel

[P114]Evaluation of correlation between microstructure and Vickers hardness of an MoSiBTiC alloy using machine learning

Session Details

[P]P107~P114

[P107]Development of a bidirectional PSP linkage model using deep generative models

[P108]Prediction of Physical Properties of Garnet-Type Structured Compounds by Machine Learning

[P109]Oxygen incorporation in MAX phases: Insights into the structures and elastic properties of the Ti-Al-C-O and Ti-Al-N-O system

[P110]Predicting Thermal Expansion Properties of Complex Composite Materials Using Multi-Scale Simulation

[P111]Prediction Model for Physical Properties of High-Entropy Alloys Using Convolutional Neural Networks

[P112]Effects of Thermal Cycling on Microscopic Structures of Fe–B–Si Amorphous Alloys: A Molecular Dynamics Study

[P113]Molecular dynamics analysis of the effect of carbon atoms on the solid phase transformation of steel

[P114]Evaluation of correlation between microstructure and Vickers hardness of an MoSiBTiC alloy using machine learning

[P112]Effects of Thermal Cycling on Microscopic Structures of Fe–B–Si Amorphous Alloys:
A Molecular Dynamics Study