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SCEJ 87th Annual Meeting (Kobe, 2022)

Last modified: 2022-03-04 12:00:00

Session programs : CS-1

Program of CS-2 is updated.
The preprints are now open (Mar. 2nd). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants in Period I/II and invited persons are required. (The participants registered in Period III will get the ID/PW on Mar. 15th.)
The yellow back on the Technical sessions page denotes streaming-live session. (HQ-21 is changed to online.)

CS-1 Frontiers of Data-driven Research and Development

Organizers: Shimada Iori (Shinshu Univ.), Koyama Michihisa, (Shinshu Univ.), Ono Tsutomu (Okayama Univ.), Toya Yoshihiro (Osaka Univ.), Yamashita Yoshiyuki (Tokyo Univ. of Agri. and Tech.), Kim Sanghong (Tokyo Univ. of Agri. and Tech.)

Data science has been rapidly developing in recent years as the fourth science following experimental science, theoretical science, and computational science. The early realization of a data-driven society led by data science has been recognized as a key to international competitiveness. This symposium will have speakers who are making pioneering efforts toward a data-driven society from various viewpoints and discuss future research and development.

Hall B, Day 2

Title / AuthorsKeywordsTopic codeAck.
Hall B(2F C201), Day 2(Mar. 17)
(10:40–12:00) (Chair: Toya Yoshihiro)
10:4011:20B204[Invited lecture] Development of smart cells using Bio Digital Transformation
(Kobe U.) (Reg)Hasunuma Tomohisa
Metabolic engineering
Enzyme engineering
11:2012:00B208[Invited lecture] Digitalization of Organic Synthesis
(Kyoto U.) Matsubara Seijiro

(13:00–14:20) (Chair: Ono Tsutomu)
13:0013:40B213[Invited lecture] Sumitomo Chemical's Materials Informatics Initiatives
(Sumitomo Chemical) *(Reg)Kaneko Shogo, (Cor)Nishino Shinya
materials Informatics
data driven R&D strategy
education and training
13:4014:20B215[Invited lecture] How to drive R&D with limited data?
(Kyoto U.) (Reg)Kano Manabu
data-driven approach
domain knowledge
(14:40–15:20) (Chair: Kim Sanghong)
14:4015:00B218Use of machine learning and feature engineering for product composition prediction in heavy oil catalytic cracking reactions
(Shinshu U.) *(Reg)Shimada Iori, (Reg)Osada Mitsumasa, (Reg)Fukunaga Hiroshi, (Reg)Koyama Michihisa
machine learning
feature engineering
catalytic cracking
15:0015:20B219Prediction of organic compound solubility for subcritical water by machine learning
(Shinshu U.) *(Reg)Osada Mitsumasa, Minesugi Yuuka, (Stu)Tamura Kotaro, (Reg)Shimada Iori
machine learning
subcritical water
(15:20–16:00) (Chair: Shimada Iori)
15:2015:40B220Inverse estimation of physical properties using physics informed neural networks in TSSG method for SiC crystal growth
(Osaka U.) *(Stu)Takehara Yuto, (Reg)Okano Yasunori
Numerical simulation
Top-Seeded Solution Growth
Physics Informed Neural Networks
15:4016:00B221Computer Automated Material Design by Universal Neural Network Potential
(Shinshu U.) *(Reg)Valadez Huerta Gerardo, Tamura Ayako, Nanba Yusuke, Hisama Kaoru, (Reg)Koyama Michihisa
Universal Neural Network Potential
Computational Material Design
(16:00–16:40) (Chair: Koyama Michihisa)
16:0016:40Panel discussion

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SCEJ 87th Annual Meeting (Kobe, 2022)

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For more information contact Organizing Committee of SCEJ 87th Annual Meeting (Kobe 2022) and IChES 2022
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