  Existing adaptive bias techniques, which seek to estimate free energies and
physical properties from molecular simulations, are limited by their reliance
on fixed kernels or basis sets which hinder their ability to efficiently
conform to varied free energy landscapes. Further, user-specified parameters
are in general non-intuitive, yet significantly affect the convergence rate and
accuracy of the free energy estimate. Here we propose a novel method wherein
artificial neural networks (ANNs) are used to develop an adaptive biasing
potential which learns free energy landscapes. We demonstrate that this method
is capable of rapidly adapting to complex free energy landscapes and is not
prone to boundary or oscillation problems. The method is made robust to
hyperparameters and overfitting through Bayesian regularization which penalizes
network weights and auto-regulates the number of effective parameters in the
network. ANN sampling represents a promising innovative approach which can
resolve complex free energy landscapes in less time than conventional
approaches while requiring minimal user input.
