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Chemical reactions occur in energy, environmental, biological, and many othernatural systems, and the inference of the reaction networks is essential tounderstand and design the chemical processes in engineering and life sciences.Yet, revealing the reaction pathways for complex systems and processes is stillchallenging due to the lack of knowledge of the involved species and reactions.Here, we present a neural network approach that autonomously discovers reactionpathways from the time-resolved species concentration data. The proposedChemical Reaction Neural Network (CRNN), by design, satisfies the fundamentalphysics laws, including the Law of Mass Action and the Arrhenius Law.Consequently, the CRNN is physically interpretable such that the reactionpathways can be interpreted, and the kinetic parameters can be quantifiedsimultaneously from the weights of the neural network. The inference of thechemical pathways is accomplished by training the CRNN with speciesconcentration data via stochastic gradient descent. We demonstrate thesuccessful implementations and the robustness of the approach in elucidatingthe chemical reaction pathways of several chemical engineering and biochemicalsystems. The autonomous inference by the CRNN approach precludes the need forexpert knowledge in proposing candidate networks and addresses the curse ofdimensionality in complex systems. The physical interpretability also makes theCRNN capable of not only fitting the data for a given system but alsodeveloping knowledge of unknown pathways that could be generalized to similarchemical systems.

Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network