  Free energy biasing methods have proven to be powerful tools to accelerate
the simulation of important conformational changes of molecules by modifying
the sampling measure. However, most of these methods rely on the prior
knowledge of low-dimensional slow degrees of freedom, i.e. Collective Variables
(CV). Alternatively, such CVs can be identified using machine learning (ML) and
dimensionality reduction algorithms. In this context, approaches where the CVs
are learned in an iterative way using adaptive biasing have been proposed: at
each iteration, the learned CV is used to perform free energy adaptive biasing
to generate new data and learn a new CV. In this paper, we introduce a new
iterative method involving CV learning with autoencoders: Free Energy Biasing
and Iterative Learning with AutoEncoders (FEBILAE). Our method includes a
reweighting scheme to ensure that the learning model optimizes the same loss at
each iteration, and achieves CV convergence. Using the alanine dipeptide system
and the solvated chignolin mini-protein system as examples, we present results
of our algorithm using the extended adaptive biasing force as the free energy
adaptive biasing method.
