Exploring Free Energy Landscapes with Neural Networks

The use of adaptive sampling algorithms is an indispensable part of modern molecular simulations. A wide array of techniques have been developed to accelerate the sampling of phase behavior and molecular conformations, with those exhibiting the proper mix of simplicity and power gaining wide acceptance within the simulation community. Here, we discuss recent efforts to incorporate machine-learning techniques, in particular, artificial neural networks, to drive sampling and efficiently obtain free energy landscapes from incomplete representations of the mean force and partition functions. We will discuss conceptual and numerical aspects of these approaches, presenting new improvements alongside applications of the algorithms to soft and biological materials.