  The present paper proposes an adaptive biasing potential for the computation
of free energy landscapes. It is motivated by statistical learning arguments
and unifies the tasks of biasing the molecular dynamics to escape free energy
wells and estimating the free energy function, under the same objective. It
offers rigorous convergence diagnostics even though history dependent,
non-Markovian dynamics are employed. It makes use of a greedy optimization
scheme in order to obtain sparse representations of the free energy function
which can be particularly useful in multidimensional cases. It employs
embarrassingly parallelizable sampling schemes that are based on adaptive
Sequential Monte Carlo and can be readily coupled with legacy molecular
dynamics simulators. The sequential nature of the learning and sampling scheme
enables the efficient calculation of free energy functions parametrized by the
temperature. The characteristics and capabilities of the proposed method are
demonstrated in three numerical examples.
