Machine Learning-Driven Multi-Scale Simulations for Green Catalysis Design
Yoshitada Morikawa(Graduate School of Engineering)

This research aims to develop Machine Learning Interatomic Potentials (MLIPs) integrated with multi-scale simulations to optimally design the heterogeneous catalysts, focusing on CO2 hydrogenation to methanol. The advantage is manifold due to the utilization of CO2 as green-house gas as well as the production of methanol as green energy resource.

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