The majority of computational catalyst design focuses on the screening of material components and alloy composition to optimize selectivity and activity for a given reaction. 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. We present CatGym, a deep reinforcement learning (DRL) environment for predicting the thermal surface reconstruction pathways and their associated kinetic barriers in crystalline solids under reaction conditions. The DRL agent 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.

大多数计算催化剂设计侧重于材料成分和合金成分的筛选，以优化给定反应的选择性和活性。然而，在实际操作条件下预测合金催化剂表面的亚稳定性需要对可能的表面重建及其相关动力学途径进行大量采样。我们展示了 CatGym，这是一种深度强化学习 (DRL) 环境，用于预测反应条件下结晶固体中的热表面重建途径及其相关的动力学障碍。DRL 代理迭代地改变近表面区域中原子的位置，以生成通向可接近的局部最小值的动力学路径，包括表面成分的变化。₃ Pd ₃ Au ₂ (111)合金催化剂。我们的结果表明，与传统的最小跳跃方法相比，DRL 代理不仅可以探索更多样化的表面成分，而且还可以生成动力学表面重建途径。我们进一步证明，我们的代理预测的全局最小能量表面组成及其相关过渡态的动力学路径与轻推弹性带计算预测的最小能量路径非常一致。