A study of Rochow-Müller reaction pathways via machine-learning potential
This study utilizes machine-learning potentials to efficiently explore reaction pathways for the Rochow-Müller process on a Cu-Si surface, revealing that while these models can underestimate specific values compared to DFT, they effectively identify promising alternative routes to M2 production by significantly accelerating the search process.