  A popular way to accelerate the sampling of rare events in molecular dynamics
simulations is to introduce a potential that increases the fluctuations of
selected collective variables. For this strategy to be successful, it is
critical to choose appropriate variables. Here we review some recent
developments in the data-driven design of collective variables, with a focus on
the combination of Fisher's discriminant analysis and neural networks. This
approach allows to compress the fluctuations of metastable states into a
low-dimensional representation. We illustrate through several examples the
effectiveness of this method in accelerating the sampling, while also
identifying the physical descriptors that undergo the most significant changes
in the process.
