  Sampling complex free energy surfaces is one of the main challenges of modern
atomistic simulation methods. The presence of kinetic bottlenecks in such
surfaces often renders a direct approach useless. A popular strategy is to
identify a small number of key collective variables and to introduce a bias
potential that is able to favor their fluctuations in order to accelerate
sampling. Here we propose to use machine learning techniques in conjunction
with the recent variationally enhanced sampling method [Valsson and Parrinello,
Physical Review Letters 2014] to determine such potential. This is achieved by
expressing the bias as a neural network. The parameters are determined in a
reinforcement learning scheme aimed at minimizing the variationally enhanced
sampling functional. This required the development of a new and more efficient
minimization technique. The expressivity of neural networks allows accelerating
sampling in systems with rapidly varying free energy surfaces, removing
boundary effects artifacts, and making one more step towards being able to
handle several collective variables.
