  Identifying reaction coordinates (RCs) from many collective variable
candidates have been of great challenge in understanding reaction mechanisms in
complex systems. Machine learning approaches, especially the deep neural
network (DNN), have become a powerful tool and are actively applied. On the
other hand, selecting the hyperparameters that define the DNN model structure
remains to be a nontrivial and tedious task. Here we develop the hyperparameter
tuning approach to determine the parameters in an automatic fashion from the
trajectory data. The DNN models are constructed to obtain a RC from many
collective variables that can adequately describe the changes of the committor
from the reactant to product. The approach is applied to study the
isomerization of alanine dipeptide in vacuum and in water. We find that
multiple DNN models with similar accuracy can be obtained, indicating that
hyperparameter space is multimodal. Nevertheless, despite the differences in
the DNN model structure, the key features extracted using the explainable AI
(XAI) tools show that all RC share the same character. In particular, the
reaction in vacuum is characterized by the dihedral angles $\phi$ and $\theta$.
The reaction in water also involves these dihedral angles as key features, and
in addition, the electrostatic potential from the solvent to the hydrogen
(H${}_{18}$) plays a role at about the transition state. The current study thus
shows that, in contrast to the diversity in the DNN models, suitably optimized
DNN models share the same features and share the common mechanism for the
reaction of interest.
