From Enhanced Sampling to Reaction Profiles

The determination of efficient collective variables is crucial to the success of many enhanced sampling methods. As inspired by previous discrimination approaches, we first collect a set of data from the different metastable basins. The data are then projected with the help of a neural network into a low-dimensional manifold in which data from different basins are well-discriminated. This is here guaranteed by imposing that the projected data follows a preassigned distribution. The collective variables thus obtained lead to an efficient sampling and often allow reducing the number of collective variables in a multibasin scenario. We first check the validity of the method in two-state systems. We then move to multistep chemical processes. In the latter case, at variance with previous approaches, one single collective variable suffices, leading not only to computational efficiency but also to a very clear representation of the reaction free-energy profile.

Automated summary

Efficient collective variables are essential for the success of enhanced sampling methods, achieved by projecting data from different metastable basins into a low-dimensional manifold using a neural network. This approach allows discrimination between basins and often reduces the number of collective variables needed. In complex chemical processes, a single collective variable can efficiently represent the reaction free-energy profile.