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Mapping out reaction pathways and their corresponding activation barriers isa significant aspect of molecular simulation. Given their inherent complexityand nonlinearity, even generating a initial guess of these paths remains achallenging problem. Presented in this paper is an innovative approach thatutilizes neural networks to generate initial guess for these reaction pathways.The proposed method is initiated by inputting the coordinates of the initialstate, followed by progressive alterations to its structure. This iterativeprocess culminates in the generation of the approximate representation of thereaction path and the coordinates of the final state. The application of thismethod extends to complex reaction pathways illustrated by organic reactions.Training was executed on the Transition1x dataset, an organic reaction pathwaydataset. The results revealed generation of reactions that bore substantialsimilarities with the corresponding test data. The method's flexibility allowsfor reactions to be generated either to conform to predetermined conditions orin a randomized manner.

Generative Model for Constructing Reaction Path from Initial to Final States