  Chemical reactions occur in energy, environmental, biological, and many other
natural systems, and the inference of the reaction networks is essential to
understand and design the chemical processes in engineering and life sciences.
Yet, revealing the reaction pathways for complex systems and processes is still
challenging due to the lack of knowledge of the involved species and reactions.
Here, we present a neural network approach that autonomously discovers reaction
pathways from the time-resolved species concentration data. The proposed
Chemical Reaction Neural Network (CRNN), by design, satisfies the fundamental
physics laws, including the Law of Mass Action and the Arrhenius Law.
Consequently, the CRNN is physically interpretable such that the reaction
pathways can be interpreted, and the kinetic parameters can be quantified
simultaneously from the weights of the neural network. The inference of the
chemical pathways is accomplished by training the CRNN with species
concentration data via stochastic gradient descent. We demonstrate the
successful implementations and the robustness of the approach in elucidating
the chemical reaction pathways of several chemical engineering and biochemical
systems. The autonomous inference by the CRNN approach precludes the need for
expert knowledge in proposing candidate networks and addresses the curse of
dimensionality in complex systems. The physical interpretability also makes the
CRNN capable of not only fitting the data for a given system but also
developing knowledge of unknown pathways that could be generalized to similar
chemical systems.
