The 105th CSJ Annual Meeting
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Academic Program [Oral A]02. Theoretical Chemistry, Chemoinformatics, and Computational Chemistry : Oral A

[[C]C302-3am1]02. Theoretical Chemistry, Chemoinformatics, and Computational Chemistry

Fri. Mar 28, 2025 9:00 AM - 9:50 AM JST
Fri. Mar 28, 2025 12:00 AM - 12:50 AM UTC
[C]C302(C302, Bldg. 2, Area 2 [3F])
9:00 AM - 9:10 AM JST(12:00 AM - 12:10 AM UTC)Japanese

[[C]C302-3am1-01]Prediction of optical properties and optimization of manufacturing conditions for glass-ceramics products with machine learning

○Makoto Takeuchi1, Yusuke Hokii2, Shigenori Akiyama2, Takuya Sato2, Hiromasa Kaneko1 (1. Meiji University, 2. GC Corporation, R&D Dept.)
9:10 AM - 9:20 AM JST(12:10 AM - 12:20 AM UTC)Japanese

[[C]C302-3am1-02]Construction of co-crystal prediction model using machine learning and design of cofomer by the model

○Manato Takeuchi1, Hiromasa Kaneko1 (1. Meiji Univ)
9:20 AM - 9:30 AM JST(12:20 AM - 12:30 AM UTC)Japanese

[[C]C302-3am1-03]Construction and Advance of Performance Prediction Models for Organic Cathode Active Materials of Lithium-Ion Battery

○Rika Yamamoto1, Yasuhiko Igarashi2, Shuntaro Miyakawa3, Taisei Sakata3, Shino Yoshizaki3, Takaya Saito3, Hiroaki Imai1, Yuya Oaki1 (1. Fac. of Sci and Tech., Keio Univ., 2. Grad. Sch. of Sys. and Info. Eng., Tsukuba, 3. SoftBank Corp.)
9:30 AM - 9:40 AM JST(12:30 AM - 12:40 AM UTC)Japanese

[[C]C302-3am1-04]Accurate Prediction of Assembled Structures of Organic Semiconductors Using Machine Learning and Molecular Simulation

○Takuya Seki1, Yudai Shinozaki1, Shunsuke Sato1, Ryosuke Ito1, Jun Takeya2,3,4, Toshihiro Okamoto4,5, Go Watanabe1,4,6,7 (1. Grad. Sch. of Sci., Kitasato Univ. , 2. Grad. Sch. of Front. Sci., Univ. of Tokyo, 3. NIMS, 4. JST CREST, 5. Sch. of Mater. and Chem. Tech., Science Tokyo, 6. Sch. of Front. Eng., Kitasato Univ., 7. KISTEC)
9:40 AM - 9:50 AM JST(12:40 AM - 12:50 AM UTC)Japanese

[[C]C302-3am1-05]Design of extraction solvents with high gold extraction capacity and low water solubility useful for gold separation and recovery using machine learning

○Takuto Tsunemi1, Tatsuya Oshima2, Hiromasa Kaneko1 (1. Meiji University, 2. The Univ. of Miyazaki)
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