E219 - Hall E, Day 2 - Hall and day program - SCEJ 55th Autumn Meeting (Sapporo, 2024)

`SCEJ 55th Autumn Meeting (Sapporo, 2024)`

Last modified: 2024-09-19 02:24:28

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Hall and day program : Hall E, Day 2 : E219

The preprints are now open. These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants and invited persons are required.

Hall E(Block B1 1F B11), Day 2(Sep. 12)

ST-22

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
ST-22 [Trans-Division Symposium] Frontiers of Data-driven Research and Development
(13:00–14:20) (Chair: Kim Sanghong)
13:00– 13:20	E213	Calculating Potential of Solar Cell on Building Façades for Japan's Energy System Optimization in 2050 (Tokyo Tech) *(Stu)Wang Shuai, (Stu)Oya Masashi, (Stu)Otoshi Natsuki, (Reg)Hamasaki Hiroshi, (Reg)Kameda Keisuke, (Reg)Manzhos Sergei, (Reg)Ihara Manabu	Solar cell facade installation Energy system optimization	ST-22	580
13:20– 13:40	E214	Prediction model for real-time power generation of solar cells with shadow simulation based on module structure and irradiance fraction (Tokyo Tech) *(Stu)Otoshi Natsuki, Kasai Yuya, (Reg)Kameda Keisuke, (Reg)Manzhos Sergei, (Reg)Ihara Manabu	Solar cell Energy system Prediction model	ST-22	865
13:40– 14:00	E215	Analysis of anion selectivity in Mg-based Layered Double Hydroxide using a Universal Neural Network Potential (Shinshu U. RISM) *(Reg)Aspera Susan Menez, Chen Yingjie, Nguyen Tien Quang, (Reg)Koyama Michihisa	machine learning layered double hydroxide (LDH) anion selectivity	ST-22	732
14:00– 14:20	E216	Molecular design using deep learning and vecror annealing (NEC) (Reg)Ishida Masahiko	Deep learning Quantum anealing Generative model	ST-22	1099
(14:40–16:00) (Chair: Sugawara Yuuki)
14:40– 15:00	E218	[The Outstanding Paper Award] Optimization of metal nanoparticle synthesis conditions using automated flow system and machine learning (AIST) *(Reg)Ono Takumi, (Reg)Takebayashi Yoshihiro, (ADMAT) Kashiwagi Tsuneo, (AIST) (Reg)Sue Kiwamu	Nanoparticle Microreactor Machine Learning	ST-22	448
15:00– 15:20	E219	Statistical consideration for investigating a direct correlation between solvent effect and hydrogenation of nitrobenzene using flow-tubular reactor (AIST) *(Reg)Wahyudianto Benny, (Reg)Yamaki Takehiro, (Reg)Hara Nobuo, (Reg)Takebayashi Yoshihiro, (Reg)Kataoka Sho	statistical analysis hydrogenation flow reactor	ST-22	121
15:20– 15:40	E220	Development of a product composition prediction model for catalytic cracking reactions and its application to the prediction of reactions with unknown feedstocks (Shinshu U.) *(Reg)Shimada Iori, Kodama Yuhei, Yasuike Shun	physics-informed machine learning catalytic cracking reaction prediction	ST-22	1128
15:40– 16:00	E221	Automated reaction process analysis using data-driven approaches (U. Tokyo/Auxilart) *(Reg)Kim Junu, (Riken) Sakata Itsushi, (Independent Researcher) Yamatsuta Eitaro, (U. Tokyo) (Reg)Sugiyama Hirokazu	Dynamic mode decomposition Chemical reaction Machine learning	ST-22	969

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SCEJ 55th Autumn Meeting (Sapporo, 2024)

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