Collective
variables (CVs) are crucial parameters in enhanced sampling
calculations and strongly impact the quality of the obtained free
energy surface. However, many existing CVs are unique to and dependent
on the system they are constructed with, making the developed CV non-transferable
to other systems. Herein, we develop a non-instructor-led deep autoencoder
neural network (DAENN) for discovering general-purpose CVs. The DAENN
is used to train a model by learning molecular representations upon
unbiased trajectories that contain only the reactant conformers. The
prior knowledge of nonconstraint reactants coupled with the here-introduced
topology variable and loss-like penalty function are
only required to make the biasing method able to expand its configurational
(phase) space to unexplored energy basins. Our developed autoencoder
is efficient and relatively inexpensive to use in terms of a priori knowledge, enabling one to automatically search
for hidden CVs of the reaction of interest.