Presentation Information

11:15 AM - 11:35 AM JST(2:15 AM - 2:35 AM UTC)

[Or47]Hybrid Sampling Approach to Machine-Learning Potentials for Hydrogen Adsorption in MOF-303

*Kartik Sau¹, Ikutaro Hamada², Tamio Ikeshoji³, Yiming Lu¹, Susmita Roy⁴, Shohichi Furukawa¹, Linda Zhang¹, Hung Ba Tran¹, Takahiro Kondo^4,5,1,6, Hao Li¹, Shin-ichi Orimo^1,7 (1. Advanced Institute for Materials Research (WPI-AIMR) Tohoku University, Aoba-ku, Sendai 980-8577, Japan (Japan), 2. Department of Precision Engineering, Graduate School of Engineering, Osaka University, Suita, Osaka (Japan), 3. Mathematics for Advanced Materials Open Innovation Laboratory (MathAM-OIL), AIST, c/o WPI-AIMR, Tohoku University, Sendai (Japan), 4. Department of Materials Science, Institute of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki (Japan), 5. Hydrogen Boride Research Center, TIAR, University of Tsukuba, Tsukuba, Ibaraki (Japan), 6. Tsukuba Research Center for Energy Materials Science, University of Tsukuba, Tsukuba, Ibaraki (Japan), 7. Institute for Materials Research, Tohoku University, Sendai, Miyagi (Japan))

Keywords:

Porous Medium,Adsorption,molecular dynamics simulations,machine learning potential,hydrogen adsorption,grand canonical Monte Carlo,density functional theory,isosteric heat

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