In this work, we present a type of molecular dynamics simulation that aims at finding, in a blind way, metastable states. Using only data coming from an initial unbiased simulation, and with the help of an appropriately defined loss function, we compute a bias that favors sampling yet unexplored configurational space regions, encouraging the system to leave the initial basin. In our work, we take advantage of what is normally thought to be a defect, namely the difficulty of neural networks to generalize. Contrary to most other enhanced sampling methods, which need previous knowledge of the reactive process, we are able to explore in a blind way metastable states, overcoming otherwise insuperable kinetic bottlenecks. We illustrate the workings of the method with a number of instructive examples.
Devergne et al. (Wed,) studied this question.