[8p-P11-1~13] 23.1 Joint Session N "Informatics" | The 86th JSAP Autumn Meeting 2025

Advanced Search

Session Details

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

[8p-P11-1~13]23.1 Joint Session N "Informatics"

Mon. Sep 8, 2025 4:00 PM - 6:00 PM JST
Mon. Sep 8, 2025 7:00 AM - 9:00 AM UTC

P11 (Gymnasium)

[8p-P11-1]Graph Convolutional Network for Predicting Atomic Charges of Various Organic

〇(B)Yugo Homma¹, Yu Homma¹, Hiroyuki Matsui¹ (1.ROEL, Yamagata Univ.)

[8p-P11-2]Machine Learning of HOMO and LUMO Energies Based on Differences Between Similar Molecules

〇(B)Hiroto Otokozawa¹, Ren Sasaki¹, Tomoharu Okada¹, Yuki Mochizuki¹, Hiroyuki Matsui¹ (1.ROEL, Yamagata Univ.)

[8p-P11-3]Machine Learning of Hamiltonian Matrices by Linear Combination of Rotation-Equivariant Matrices for Rapid Quantum Chemistry Calculations

〇(B)Jintaro Endo¹, Koki Ozawa¹, Hiroyuki Matsui¹ (1.ROEL, Yamagata Univ)

[8p-P11-4]Analysis of Gas Molecule Diffusion Behavior in Titanium-Silica Membranes Using Machine-Learning Molecular Dynamics Simulations

〇Meguru Yamazaki¹, Yuta Yoshimoto¹, Isshin Gosha², Taku Fujisawa², Tomoya Matsuda^1,2, Naoki Matsumura¹, Yuto Iwasaki¹, Atsuki Inoue², Tomohisa Yoshioka², Hiroshi Kawaguchi², Yasufumi Sakai¹ (1.Fujitsu Limited, 2.Kobe University)

[8p-P11-5]Prediction of Formation Energy of Oxygen Defects in SrTiO₃ Using Machine-Learning Molecular Dynamics Simulations

〇Kazutaka Nishiguchi^1,2, Ryota Yamamoto², Meguru Yamazaki¹, Naoki Matsumura¹, Yuta Yoshimoto¹, Seiichiro Ten-no², Yasufumi Sakai¹ (1.Fujitsu Ltd., 2.Kobe Univ.)

The 86th JSAP Autumn Meeting 2025

Session Details

[8p-P11-1~13]23.1 Joint Session N "Informatics"

[8p-P11-1]Graph Convolutional Network for Predicting Atomic Charges of Various Organic

[8p-P11-2]Machine Learning of HOMO and LUMO Energies Based on Differences Between Similar Molecules

[8p-P11-3]Machine Learning of Hamiltonian Matrices by Linear Combination of Rotation-Equivariant Matrices for Rapid Quantum Chemistry Calculations

[8p-P11-4]Analysis of Gas Molecule Diffusion Behavior in Titanium-Silica Membranes Using Machine-Learning Molecular Dynamics Simulations

[8p-P11-5]Prediction of Formation Energy of Oxygen Defects in SrTiO₃ Using Machine-Learning Molecular Dynamics Simulations

[8p-P11-6]Atomic Level Explanation via PFP Descriptors and Shapley Values

[8p-P11-7]Machine Learning for High-Performance GaN-HEMTs: Optimizing amorphous SiN Thin Film Deposition

[8p-P11-8]Optimization of Semiconductor Drying Process Using AI-Driven Process Evaluation Metrics

[8p-P11-9]Process Informatics of Ion Beam Irradiated Semiconductor Nanostructures Visualized by Persistent Homology

[8p-P11-10]Image Analysis of Polarized Light Microscopy Using Convolutional Neural Networks

[8p-P11-11]Materials Development with LLM (III): Extraction of Materials Data from Scientific Articles

[8p-P11-12]Extraction of Growth Layer Structures from Research Papers Using a Local LLM

[8p-P11-13]Developing a Support System for Materials Scientists through LLM–Simulator Synergy

Session Details

[8p-P11-1~13]23.1 Joint Session N "Informatics"

[8p-P11-1]Graph Convolutional Network for Predicting Atomic Charges of Various Organic

[8p-P11-2]Machine Learning of HOMO and LUMO Energies Based on Differences Between Similar Molecules

[8p-P11-3]Machine Learning of Hamiltonian Matrices by Linear Combination of Rotation-Equivariant Matrices for Rapid Quantum Chemistry Calculations

[8p-P11-4]Analysis of Gas Molecule Diffusion Behavior in Titanium-Silica Membranes Using Machine-Learning Molecular Dynamics Simulations

[8p-P11-5]Prediction of Formation Energy of Oxygen Defects in SrTiO3 Using Machine-Learning Molecular Dynamics Simulations

[8p-P11-6]Atomic Level Explanation via PFP Descriptors and Shapley Values

[8p-P11-7]Machine Learning for High-Performance GaN-HEMTs: Optimizing amorphous SiN Thin Film Deposition

[8p-P11-8]Optimization of Semiconductor Drying Process Using AI-Driven Process Evaluation Metrics

[8p-P11-9]Process Informatics of Ion Beam Irradiated Semiconductor Nanostructures Visualized by Persistent Homology

[8p-P11-10]Image Analysis of Polarized Light Microscopy Using Convolutional Neural Networks

[8p-P11-11]Materials Development with LLM (III): Extraction of Materials Data from Scientific Articles

[8p-P11-12]Extraction of Growth Layer Structures from Research Papers Using a Local LLM

[8p-P11-13]Developing a Support System for Materials Scientists through LLM–Simulator Synergy

[8p-P11-5]Prediction of Formation Energy of Oxygen Defects in SrTiO₃ Using Machine-Learning Molecular Dynamics Simulations