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.