Impact of amorphous structure on CO2 electrocatalysis with Cu: A combined machine learning forcefield and DFT modelling approach

Amorphous materials hold significant promise for enhancing electrocatalytic CO 2 reduction (CO 2 R) performance, but their intricate structures present challenges in understanding their behaviour. We present a computational investigation combining machine learning force fields and DFT calculations to explore amorphous copper (Cu) as a potential catalyst for the CO 2 R to C 1 and C 2 products. Our study reveals that amorphous Cu surfaces, compared to crystalline counterparts, offer a wider range of coordination sites, leading to a multitude of active centres for CO 2 adsorption. Notably, some investigated surfaces spontaneously activate CO 2 , demonstrating their potential for efficient conversion. Furthermore, the intermediates of the CO 2 R on these surfaces exhibit enhanced stability, translating to lower overpotentials and improved selectivity. This work paves the way for further research and development in using amorphous Cu-based catalysts for sustainable CO 2 conversion technologies, offering significant potential for mitigating climate change.