  On the time scales accessible to atomistic numerical modelling, chemical
reactions are considered rare events. Atomistic simulations are typically
biased along a low-dimensional representation of a chemical reaction in an
atomic structure space, i.e., along the collective variable, to accelerate
sampling of these improbable events. However, suitable collective variables are
often complicated to guess due to the complexity of the transitions. Therefore,
we present an automatic method of generating robust collective variables from
atomic representation vectors, using either fixed Behler-Parrinello functions
or representations extracted from pre-trained machine learning potentials.
Variational autoencoder with these representations as inputs is trained while
its latent space with arbitrary dimension gives us the set of collective
variables. The resulting collective variables inherit all necessary invariances
from the atomic representations and can be trained entirely unsupervised. The
method's effectiveness is demonstrated using three different chemical
reactions, one being the complex hydrolysis of a heterogeneous aluminosilicate
catalyst. Lastly, we consider the method in the context of unseen atomic
structure prediction, efficiently creating structures for different values of
collective variables in a generative model fashion.
