[1P01-32] Poster session

[1P29] Theoretical Study of Solvent Effects in Copper–Histidine Composite Electrocatalysts Using Machine Learning Interatomic Potential and Density Functional Theory

*Takuto Kusakawa¹, Kohei Tada^1,2, Ryohei Kishi^1,2,3, Yasutaka Kitagawa^1,2,3,4 (1. Graduate School of Engineering Science, The University of Osaka (Japan), 2. ICS-OTRI, The University of Osaka (Japan), 3. QIQB, The University of Osaka (Japan), 4. OTRI-Spin, The University of Osaka (Japan))

Electrochemical CO₂ reduction reaction (CO₂RR) is a promising technology for converting CO₂ into valuable chemicals. Cu catalysts can produce hydrocarbons such as CH₄ and C₂H₄, but their high overpotentials and low product selectivity remain challenges. Histidine modification has been reported to improve CO₂RR efficiency, whereas the role of explicit water molecules has not been sufficiently clarified. In this study, we used a machine learning interatomic potential and DFT calculations to analyze histidine adsorption structures and solvent effects. The results showed that water changes the relative stability of histidine adsorption structures. Molecular dynamics simulations also suggested that histidine will be involved in proton-transfer processes during CO₂RR.

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