Generative Model for Constructing Reaction Path from Initial to Final
  States
Akihide Hayashi So Takamoto Ju Li Daisuke Okanohara
Mapping out reaction pathways and their corresponding activation barriers is
a significant aspect of molecular simulation. Given their inherent complexity
and nonlinearity, even generating a initial guess of these paths remains a
challenging problem. Presented in this paper is an innovative approach that
utilizes neural networks to generate initial guess for these reaction pathways.
The proposed method is initiated by inputting the coordinates of the initial
state, followed by progressive alterations to its structure. This iterative
process culminates in the generation of the approximate representation of the
reaction path and the coordinates of the final state. The application of this
method extends to complex reaction pathways illustrated by organic reactions.
Training was executed on the Transition1x dataset, an organic reaction pathway
dataset. The results revealed generation of reactions that bore substantial
similarities with the corresponding test data. The method's flexibility allows
for reactions to be generated either to conform to predetermined conditions or
in a randomized manner.