2010/11/10 by Ilias Bilionis, Bilionis, I., Phaedon‐Stelios Koutsourelakis +1 · 1 citation
Decision Sciences · Physics and Astronomy · #Computation (stat.CO) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Mathematical Physics (math-ph) #Probabilistic and Robust Engineering Design #Statistical Mechanics and Entropy #Theoretical and Computational Physics
paper · pdf · doi:10.48550/arxiv.1011.2416
openalex publication_date 2010/11/10 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
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.