[P] P37~P45

Tue. Sep 19, 2023 1:00 PM - 2:30 PM Hall, 1st Flr. at "TOYAMA Jiyukan " (Hall, 1st Flr. at "TOYAMA Jiyukan ")

※表示の講演時間には質疑応答時間も含みます。
（質疑応答時間5分、基調講演と招待講演は5～10分）

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1:00 PM - 2:30 PM

[P37] Prediction of material properties for a small dataset using the pre-trained GNN model

*Kento NISHIO¹, Kiyou SHIBATA², Teruyasu MIZOGUCHI² (1. Sch. Eng., the Univ. of Tokyo, 2. IIS, the Univ. of Tokyo)

1:00 PM - 2:30 PM

[P38] Creation of high-density Ti-H sintered alloy using simulation with AI technology

*Yushi MORI¹, Yasuhiro Hoshiyama², Tomoki Maruoka³ (1. 関西大学（院生）、2. 関西大学、3. 京都市産業技術研究所)

1:00 PM - 2:30 PM

[P39] Image segmentation analysis of MoSiBTiC microstructure by machine learning

*Chihana KUDO¹, Xinyu YAN¹, Kyosuke YOSHIMI² (1. Tohoku University, Graduate School of Engineering (Graduate student), 2. Tohoku University, Graduate School of Engineering)

1:00 PM - 2:30 PM

[P40] Prediction of mechanical properties of high-entropy alloys using machine learning

*Genki SATO¹, Kazuki Utugi¹, Tomoyuki Yamamoto¹ (1. Waseda Univ.)

1:00 PM - 2:30 PM

[P41] Chemical order promoted vacancy and interstitial sluggish diffusion in CrCoNi medium-entropy alloy

*Yangen Li¹, Shigenobu Ogata¹ (1. Osaka Univ.)

1:00 PM - 2:30 PM

[P42] Quantum Monte Carlo Method for Formation Energies Point Defects in Si

*Masahiro YAMADA¹, Tatsuya YOKOI², Yu OGURA², Katsuyuki MATSUNAGA^2,3 (1. 名大工(院生)、2. 名大工、3. JFCC)

1:00 PM - 2:30 PM

[P43] Molecular dynamics simulation of energy states of triple junctions

*Kentaro SHIMIZU¹, Naoki MIYAZAWA², Susumu ONAKA³ (1. Tokyo Institute of Technology School of Materials and Chemical Technology, 2. Kyoto University Graduate School of Energy Science, 3. Tokyo Institute of Technology School of Materials and Chemical Technology)

1:00 PM - 2:30 PM

[P44] Trainable atomic descriptor for improving predictive ability of artificial-neural-network interatomic potential

*Masami UCHIDA¹, Tatsuya YOKOI², Yu OGURA², Katsuyuki MATSUNAGA^2,3 (1. 名大工(院生)、2. 名大工、3. JFCC)

1:00 PM - 2:30 PM

[P45] Zr segregation structure at a grain boundary in α-Al₂O3 by neural network interatomic potential

*Yuki NAKANO¹, Tatsuya Yokoi², Yu Ogura², Katsuyuki Matsunaga^2,3 (1. 名古屋大工(院生)、2. 名古屋大工、3. JFCC)

Session information

[P] P37~P45

[P37] Prediction of material properties for a small dataset using the pre-trained GNN model

[P38] Creation of high-density Ti-H sintered alloy using simulation with AI technology

[P39] Image segmentation analysis of MoSiBTiC microstructure by machine learning

[P40] Prediction of mechanical properties of high-entropy alloys using machine learning

[P41] Chemical order promoted vacancy and interstitial sluggish diffusion in CrCoNi medium-entropy alloy

[P42] Quantum Monte Carlo Method for Formation Energies Point Defects in Si

[P43] Molecular dynamics simulation of energy states of triple junctions

[P44] Trainable atomic descriptor for improving predictive ability of artificial-neural-network interatomic potential

[P45] Zr segregation structure at a grain boundary in α-Al₂O3 by neural network interatomic potential

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