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# Iterative Unbiasing of Quasi-Equilibrium Sampling

journal contribution

posted on 2019-12-05, 03:29 authored by F. Giberti, B. Cheng, G. A. Tribello, M. CeriottiAtomistic modeling
of phase
transitions, chemical reactions, or other rare events that involve
overcoming high free energy barriers usually entails prohibitively
long simulation times. Introducing a bias potential as a function
of an appropriately chosen set of collective variables can significantly
accelerate the exploration of phase space, albeit at the price of
distorting the distribution of microstates. Efficient reweighting
to recover the unbiased distribution can be nontrivial when employing
adaptive sampling techniques such as metadynamics, variationally enhanced
sampling, or parallel bias metadynamics, in which the system evolves
in a quasi-equilibrium manner under a time-dependent bias. We introduce
an iterative unbiasing scheme that makes efficient use of all the
trajectory data and that does not require the distribution to be evaluated
on a grid. The method can thus be used even when the bias has a high
dimensionality. We benchmark this approach against some of the existing
schemes on model systems with different complexity and dimensionality.

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adaptive sampling techniquestrajectory datadimensionalitymodel systemsIterative Unbiasingphase transitionsiterative unbiasing schemeQuasi-Equilibrium Sampling Atomistic modelingquasi-equilibrium mannerbias metadynamicschemical reactionssimulation timesEfficient reweightingenergy barrierstime-dependent biasphase space

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