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Multiscale, Multiphysics Computational Chemistry Methods Based on Artificial Intelligence Integrated Ultra-Accelerated Quantum Molecular Dynamics for the Application to Automotive Emission Control
- Akira Miyamoto - Tohoku Univ. ,
- Kenji Inaba - Tohoku Univ. ,
- Yukie Ishizawa - Tohoku Univ. ,
- Manami Sato - Tohoku Univ. ,
- Rei Komuro - Tohoku Univ. ,
- Masashi Sato - Tohoku Univ. ,
- Ryo Sato - Tohoku Univ. ,
- Patrick Bonnaud - Tohoku Univ. ,
- Ryuji Miura - Tohoku Univ. ,
- Ai Suzuki - Tohoku Univ. ,
- Naoto Miyamoto - Tohoku Univ. ,
- Nozomu Hatakeyama - Tohoku Univ. ,
- Masanori Hariyama - Tohoku Univ.
ISSN: 1946-3936, e-ISSN: 1946-3944
Published November 08, 2016 by SAE International in United States
Citation: Miyamoto, A., Inaba, K., Ishizawa, Y., Sato, M. et al., "Multiscale, Multiphysics Computational Chemistry Methods Based on Artificial Intelligence Integrated Ultra-Accelerated Quantum Molecular Dynamics for the Application to Automotive Emission Control," SAE Int. J. Engines 9(4):2434-2441, 2016, https://doi.org/10.4271/2016-32-0067.
On the basis of extensive experimental works about heterogeneous catalysts, we developed various software for the design of automotive catalysts such as Ultra-Accelerated Quantum Chemical Molecular Dynamics (UA-QCMD), which is 10 million times faster than the conventional first principles molecular dynamics, mesoscopic modeling software for supported catalysts (POCO2), and mesoscopic sintering simulator (SINTA) to calculate sintering behavior of both precious metals (e.g., Pt, Pd, Rh) and supports (e.g., Al2O3, ZrO2, CeO2, or CeO2-ZrO2). We integrated the previous programs in a multiscale, multiphysics approach for the design of automotive catalysts. The method was efficient for a variety of important catalytic reactions in the scope of the automotive emission control. We demonstrated the efficiency of our approach by comparing our data with experimental results including both simple laboratory experiments and chassis dynamometer exhaust gas emission control experiments. We also demonstrated that the UA-QCMD method is an efficient tool for the estimation of mesoscopic sintering activation energies for both precious metals and supports. On the basis of our successful applications of the UA-QCMD to various important chemical processes of exhaust emission controls and sintering predictions of both precious metals and support of automotive catalysts, we employed in the present study artificial intelligence to determine fundamental parameters from all electron density functional methods and thermodynamic results. This new technique was proven highly efficient for optimizing parameters necessary in our simulations.