We present an automated method that maps surface reaction pathways without using experimental data and human assumptions. In this method, bias potentials promoting surface reactions are applied to enable statistical samplings of the surface reaction within the timescale of ab-initio MD simulations, and elementary reactions are extracted automatically using an extension of the method constructed for gas- or liquid-phase reactions. By converting the extracted elementary reaction data to directed graph data, MD trajectories can be efficiently mapped onto reaction pathways using a network analysis tool. To demonstrate the power of the method, it was applied to the steam reforming of methane on the Rh(111) surface and to propane reforming on the Pt(111) and Pt3Sn(111) surfaces. We discover new energetically favorable pathways for both reactions and reproduce the experimentally-observed materials-dependence of the surface reaction activity and the selectivity for the propane reforming reactions.

Discovering surface reaction pathways using accelerated molecular dynamics and network analysis tools

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