Free energy computations by minimization of Kullback–Leibler divergence: An efficient adaptive biasing potential method for sparse representations

I. Bilionis, Phadeon-Stelios Koutsourelakis

    Research output: Contribution to journalArticle

    27 Citations (Scopus)

    Abstract

    The present paper proposes an adaptive biasing potential technique 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 of minimizing the Kullback–Leibler divergence between appropriately selected densities. 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.




    Original languageEnglish
    Pages (from-to)3849-3870
    JournalJournal of Computational Physics
    Volume231
    Issue number9
    DOIs
    Publication statusPublished - 2012

    Keywords

    • Free energy computations
    • Adaptive biasing potential
    • Sequential Monte Carlo
    • Atomistic simulations
    • Statistical learning

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