[A5-P501-01] Analysis of trade-off relations between magnetization, Curie temperature and price in ThMn₁₂-type Magnet compounds and multi-objective Bayesian optimization for obtaining Pareto Front

*Taro Fukazawa¹, Takashi Miyake¹ (1. National Institute of Advanced Industrial Science and Technology (Japan))

[A5-P501-02] Theory towards Predicting the Lifetime of Electrocatalysis

*Hideshi Ooka¹, Marie E Wintzer¹, Hirokazu Komatsu², Kiyohiro Adachi¹, Ailong Li¹, Shuang Kong¹, Daisuke Hashizume¹, Atsushi Mochizuki³, Ryuhei Nakamura^1,4 (1. RIKEN (Japan), 2. Toyota Natl. College of Tech. (Japan), 3. Kyoto Univ. (Japan), 4. Tokyo Inst. Tech. (Japan))

[A5-P501-03] Global Optimization of Atomic Arrangement in Multicomponent Alloys Accelerated by Universal Neural Network Potential

*Tien Quang Nguyen¹, Michihisa Koyama¹ (1. Shinshu Univ. (Japan))

[A5-P501-04] Kinetic Monte Carlo Simulation using Machine-Learned Potentials for Assessing Durability of Platinum-Based Alloy Nanoparticle Catalysts for Fuel Cells

*Deokgi Hong¹, Jisu Jung¹, Seungwu Han¹ (1. Seoul National Univ. (Korea))

[A5-P501-05] Atmospheric Corrosion Prediction of Carbon Steel Based on Improved Double Hidden Layer Neural Network

*Peng Shi¹, Huike Zhang¹, Liwu Jiang¹ (1. University of Science and Technology Beijing (China))

[A5-P501-06] A Neural Network Interatomic Potential for α-Iron and Carbon Binary System

*Fan-Shun Meng¹, Shigenobu Ogata¹ (1. Osaka Univ. (Japan))

[A5-P501-07] Molecular Dynamics Study on HF Etching of amorphous Si₃N₄ with Neural Network Potential

*Changho Hong¹, Sangmin Oh¹, Seungwu Han¹ (1. Seoul National Univ. (Korea))

[A5-P501-08] Atomistic study of TaN_x-Cu Interfaces using Neural Network Interatomic Potentials

*JeongMin Choi¹, Jaehoon Kim¹, Seungwu Han¹ (1. Seoul National Univ. (Korea))

[A5-P501-09] New Paradigms in Machine Learning for Materials Design: ML Forcefields, De Novo Design, and LLMs

*Anand Chandrasekaran¹, Garvit Agarwal¹, H. Shaun Kwak², Tsuguo Morisato³, Mathew D. Halls⁴, Karl Leswing¹ (1. Schrödinger Inc., New York, New York, 10036 (United States of America), 2. Schrödinger Inc., Portland, Oregon, 97204 (United States of America), 3. Schrödinger K.K., Chiyoda-ku, Tokyo 100-0005 (Japan), 4. Schrödinger Inc., San Diego, California, 92121 (United States of America))

[A5-P501-10] Machine Learning Molecular Dynamics Simulation of CO-driven Formation of Cu Clusters on Cu(111) Surface

*Harry Handoko Halim¹, Ryo Ueda¹, Yoshitada Morikawa¹ (1. Osaka University (Japan))

[A5-P501-11] Prediction of density of states for multinary nanoalloys

*Yusuke Nanba¹, Michihisa Koyama¹ (1. Shinshu Univ. (Japan))

[A5-P501-12] Application of LSTM machine learning to battery emulators

*Kanato Oka¹, Naoto Tanibata¹, Hayami Takeda¹, Masanobu Nakayama¹ (1. Nagoya Institute of Technology (Japan))

[A5-P501-13] Crystal Structure Prediction for Li-Mg-P₂O₇ Using First-principles Calculations

*Takuma Otani¹, Takumi Sato¹, Shogo Nakamori¹, Futoshi Utsuno², Tsuyoshi Honma¹, Tomoki Yamashita¹ (1. Nagaoka Univ. Tech. (Japan), 2. Idemitsu Kosan Co.,Ltd. (Japan))

[A5-P501-14] Topological Data Analysis for Feature Extraction of Phase-separated Structures in Proton Exchange Membranes

*Yukito Higashi¹, Tsuyohiko Fujigaya^1,2,3, Koichiro Kato^1,2 (1. Department of Applied Chemistry, Graduated School of Engineering, Kyushu Univ. (Japan), 2. Center of Molecular systems, Kyushu Univ. (Japan), 3. Int'l Inst. for Carbon-Neutral Energy Res., Kyushu Univ. (Japan))

[A5-P501-15] Ab-initio Electrical Conductivity Calculations of Ag-Pd Alloy based on Wannier-CPA Method

*Shota Namerikawa¹, Takashi Koretsune¹ (1. Department of Physics, Tohoku University (Japan))

[A5-P501-16] QSGW calculations for thousands of materials to predict accurate band gaps

*Shota Takano¹, Katsuhiro Suzuki¹, Kazunori Sato^1,2,3, Takao Kotani^2,4 (1. Osaka Univ. (Japan), 2. CSRN, Osaka Univ. (Japan), 3. OTRI, Osaka Univ. (Japan), 4. Tottori Univ. (Japan))

[A5-P501-17] High-performance quadratic spectral neighbor analysis potential for Al-H binary system to investigate hydrogen embrittlement

*Yuxi Liu¹, Koji Shimizu¹, Satoshi Watanabe¹ (1. UTokyo (Japan))

[A5-P501-18] Efficient stacking pattern search for van der Waals hetero structures via Bayesian optimization

*Beomgyu Choi¹, Tsuyohiko Fujigaya^1,2,3, Koichiro Kato^1,3 (1. Graduate School of Eng., Kyushu Univ. (Japan), 2. Int'l. Inst. for Carbon-Neutral Energy Res., Kyushu Univ. (Japan), 3. Center of Molcule Systems, Kyushu Univ. (Japan))

[A5-P501-19] Graph Neural Network Molecular Dynamics Study of Thermal Degradation of Clean and Oxygen-terminated Diamond (111) and (100) Surfaces

*John Isaac Guinto Enriquez¹, Harry Handoko Halim¹, Takahiro Yamasaki², Masato Michiuchi², Kouji Inagaki¹, Masaaki Geshi³, Ikutaro Hamada¹, Yoshitada Morikawa^1,4 (1. Graduate School of Engineering, Osaka Univ. (Japan), 2. Advanced Materials Laboratory, Sumitomo Electric Industries Ltd. (Japan), 3. R^3 Institute for Newly-Emerging Science Design, Osaka Univ. (Japan), 4. Research Center for Precision Engineering, Graduate School of Engineering, Osaka Univ. (Japan))

[A5-P501-20] Material design and condition optimization for generating higher force of photomechanical crystals by machine learning

*Kazuki Ishizaki¹, Toru Asahi¹, Takuya Taniguchi¹ (1. Waseda Univ. (Japan))

[A5-P501-21] Developing Machine Learning Lattice Optimization Methods Based On Crystal Graph Convolutional Neural Network

*Sang woo Park¹, Jong-Goo Park¹, Ki-Ha Hong¹ (1. Hanbat national Univ. (Korea))

[A5-P501-22] Efficient Exploration of Organic Anode Active Materials for Lithium-ion Battery with Advances in Capacity Prediction Models

*Haruka Tobita¹, Yuki Namiuchi², Hiroaki Imai¹, Koki Obinata³, Masato Okada³, Yasuhiko Igarashi², Yuya Oaki¹ (1. Keio Univ. (Japan), 2. Univ. of Tsukuba (Japan), 3. Univ. of Tokyo (Japan))

[A5-P501-23] Effect of Data Size on Extraction of Descriptors using Sparse Modeling for Small Data: Three Predictors for Controlled Synthesis of Exfoliated Nanosheets

*Yuka Kitamura¹, Yasuhiko Igarashi², Hiroaki Imai¹, Yuya Oaki¹ (1. Keio Univ. (Japan), 2. Univ. of Tsukuba (Japan))

[A5-P501-24] Organization of computational and experimental metadata using function decomposition trees

*Hiori Kino¹ (1. NIMS (Japan))

[A5-P501-25] Developing Smart Floor Sensor using Monitoring Automation Mapping Algorithm for Healthcare Application

*Hyunsoo Kim¹, Soojin Ryu¹, Ji Soo Son¹, Hae Inn Lee¹, Seong Jin Jang¹ (1. Korea Inst. of Indus. Tech. (Korea))

[A5-P501-26] Interface tool from Wannier90 to RESPACK: wan2respack

Kensuke Kurita², Takahiro Misawa¹, Kazuyoshi Yoshimi¹, *Kota Ido¹, Takashi Koretsune² (1. ISSP, Univ. of Tokyo (Japan), 2. Dept. of Phys., Tohoku Univ. (Japan))

[A5-P501-27] A spectral preprocessing method using piecewise regression

*Shinjiro Yagyu¹, Takahiro Nagata¹ (1. NIMS (Japan))

[A5-P501-28] Machine Learning and Computational Modeling for Predicting the Deformation of Natural Fiber-Reinforced Polymer Composites in Bulletproof Material Prototyping

*Isaiah Nikolo Gonzales¹, John Henry Galino¹, Eduardo Magdaluyo, Jr.², Paul Rossener Regonia¹ (1. Univ. of the Philippines Diliman, Dept. of Computer Science (Philippines), 2. Univ. of the Philippines Diliman, Dept. of Mining, Metallurgical, and Materials Engineering (Philippines))

[A5-P501-29] Bayesian Spectroscopy for X-ray Absorption and X-ray Magnetic Circular Dichroism Spectra Measured on a Nickel Thin Film

*Taiga Yamasaki^1,2, Iwamitsu Kazunori¹, Hiroyuki Kumazoe³, Masahiro Sawada⁴, Masahiro Hara¹, Ichiro Akai¹ (1. Kumamoto Univ. (Japan), 2. Res. Fellow of Japan Society for the Promotion of Sci. (Japan), 3. Hitotsubashi Univ. (Japan), 4. Hiroshima Univ. (Japan))

[A5-P501-30] Deep learning model for data compression of Wannier functions

*Tatsuki Oikawa¹, Kota Ido², Takahiro Misawa², Takashi Koretsune¹, Kazuyoshi Yoshimi² (1. Dept. of Phys., Tohoku Univ. (Japan), 2. ISSP, Univ. of Tokyo (Japan))

[A5-P501-31] Automatic Magnetic Data Extraction from Tables in Scientific Documents to Create a Magnet Database

*Hiroyuki Oka¹, Masashi Ishii¹ (1. National Institute for Materials Science (Japan))

[A5-P501-32] SpM: Sparse Modeling Method/Tool for Analytical Continuation of Imaginary-time Green's Function

*Yuichi Motoyama¹, Hiroshi Shinaoka², Junya Otsuki³, Kazuyoshi Yoshimi¹ (1. The Inst. for Solid State Physics, The Univ. of Tokyo (Japan), 2. Saitama Univ. (Japan), 3. Okayama Univ. (Japan))

[A5-P501-33] A Framework to Investigate Motion Behaviors of Particles in Heterogeneous Media

*Anh Duc Dao¹, Chi Hieu Dam¹ (1. Japan Advanced Institute of Science and Technology (Japan))

[A5-P501-34] Identification of Structural Information from X-ray CT Images Based on Topological Data Analysis

*Xichan Gao¹, Kazuto Akagi¹, Daiki Kido^2,3, Masao Kimura^2,3 (1. Advanced Institute for Materials Research (AIMR), Tohoku Univ. (Japan), 2. High Energy Accelerator Research Organization (KEK) (Japan), 3. SOKENDAI (The Graduate Univ. for Advanced Studies) (Japan))

[A5-P501-35] Combining MLFF with DFT: Examples of MXenes and TMDs

*ADISAK BOONCHUN¹ (1. Kasetsart University (Thailand))

[A5-P501-36] Deep learning for phase retrieval from virtual experiments in coherent X-ray diffraction imaging

*Sinh Tien Vu¹, Dam Hieu Chi¹ (1. Japan Advanced Institute of Science and Technology (Japan))

[A5-P501-37] An evidence-based framework to measure similarity between materials with considering uncertainty for discovering novel materials

*Minh Quyet Ha¹, Hieu-Chi Dam¹, Duong-Nguyen Nguyen¹ (1. Japan Advanced Institute of Science and Technology (Japan))

[A5-P501-38] Machine learning-aided Genetic Algorithm in investigating structure-stability relationship of SmFe₁₂ based structures

*Duong-Nguyen Nguyen¹, Hiori Kino², Takahashi Miyake³, Hieu-Chi Dam¹ (1. Japan Advanced Institute of Science and Technology (Japan), 2. National Institute for Materials Science (Japan), 3. National Institute of Advanced Industrial Science and Technology (Japan))

[A5-P501-39] Material ontology-based data linkage in “Data Creation and Utilization-Type Material Research and Development Project (DxMT)”: Approach from Magnetic Materials Research

*Masashi Ishii¹, Taisuke Sasaki¹, Yukiko Takahashi¹, Keitaro Sodeyama¹, Tadakatsu Ohkubo¹ (1. NIMS (Japan))

[A5-P501-40] Quantitative expression of electrode potential by machine learning analysis and liquid Madelung potential

*Norio Takenaka¹, Seongjae Ko¹, Atsushi Kitada¹, Masanobu Nakayama², Atsuo Yamada¹ (1. The Univ. of Tokyo (Japan), 2. Nagoya Inst. of Tech. (Japan))

[A5-P501-41] Mathematical Framework for Efficient Prediction of Local Strain Field in Binary Composites Using Low Rank Approximation.

*PRABHAT KARMAKAR¹, SAYAN GUPTA^1,2, ILAKSH ADLAKHA¹ (1. Department of Applied Mechanics, Indian Inst of Tech. Madras (India), 2. Complex Systems and Dynamics Group, Indian Inst of Tech. Madras (India))

[A5-P501-42] Advancement of software usability toward materials design on large-scale computers

*Tatsumi Aoyama¹, Yuichi Motoyama¹, Kazuyoshi Yoshimi¹, Naoki Kawashima¹ (1. Inst. for Solid State Physics, Univ. of Tokyo (Japan))

Session information

[A5-P501] Poster 501

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