Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy

The screening of material components and alloy composition to optimize selectivity and activity for a given reaction is a major focus of the computational catalyst design. However, predicting the metastability of the alloy catalyst surface at realistic operating conditions requires an extensive sampling of possible surface reconstructions and their associated kinetic pathways. In this work, we propose CatGym, a deep reinforcement learning (DRL) environment for the prediction of the thermal surface reconstruction pathways and their associated kinetic barriers in crystalline solids under reaction conditions. Within the CatGym environment enables the DRL agent to iteratively changes the positions of atoms in the near-surface region to generate kinetic pathways to accessible local minima involving changes in the surface compositions. We showcase our agent by predicting the surface reconstruction pathways of a ternary Ni3Pd3Au2(111) alloy catalyst. Our results show that the DRL agent can not only explore more diverse surface compositions than the conventional minima hopping method, but also generate the kinetic surface reconstruction pathways. We further demonstrate that the kinetic pathway to a global minimum energy surface composition and its associated transition state predicted by our agent is in good agreement with the minimum energy path predicted by nudged elastic band calculations.