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 CO2 reduction (CO2R) 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 CO2R to C1 and C2 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 CO2 adsorption. Notably, some investigated surfaces spontaneously activate CO2, demonstrating their potential for efficient conversion. Furthermore, the intermediates of the CO2R 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 CO2 conversion technologies, offering significant potential for mitigating climate change.