Discovering Catalytic Reaction Networks Using Deep Reinforcement Learning from First-Principles.

Determining the reaction pathways, which is central to illustrating the working mechanisms of a catalyst, is severely hindered by the high complexity of the reaction and the extreme scarcity of the data. Here, we develop a novel artificial intelligence framework integrating deep reinforcement learning (DRL) techniques with density functional theory simulations to automate the quantitative search and evaluation on the complex catalytic reaction networks from zero knowledge. Our framework quantitatively transforms the first-principles-derived free energy landscape of the chemical reactions to a DRL environment and the corresponding actions. By interacting with this dynamic environment, our model evolves by itself from scratch to a complete reaction path. We demonstrate this framework using the Haber-Bosch process on the most active Fe(111) surface. The new path found by our framework has a lower overall free energy barrier than the previous study based on domain knowledge, demonstrating its outstanding capability in discovering complicated reaction paths. Looking forward, we anticipate that this framework will open the door to exploring the fundamental reaction mechanisms of many catalytic reactions.