Iterative unbiasing of quasi-equilibrium sampling
Abstract
Atomistic modelling 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 re-weighting 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 models systems with different complexities and dimensionalities.
Cite
@article{arxiv.1911.01140,
title = {Iterative unbiasing of quasi-equilibrium sampling},
author = {Federico Giberti and Bingqing Cheng and Gareth Aneurin Tribello and Michele Ceriotti},
journal= {arXiv preprint arXiv:1911.01140},
year = {2019}
}