Targeted free energy estimation via learned mappings
- URL: http://arxiv.org/abs/2002.04913v2
- Date: Tue, 18 Aug 2020 20:15:11 GMT
- Title: Targeted free energy estimation via learned mappings
- Authors: Peter Wirnsberger, Andrew J. Ballard, George Papamakarios, Stuart
Abercrombie, S\'ebastien Racani\`ere, Alexander Pritzel, Danilo Jimenez
Rezende and Charles Blundell
- Abstract summary: Free energy perturbation (FEP) was proposed by Zwanzig more than six decades ago as a method to estimate free energy differences.
FEP suffers from a severe limitation: the requirement of sufficient overlap between distributions.
One strategy to mitigate this problem, called Targeted Free Energy Perturbation, uses a high-dimensional mapping in configuration space to increase overlap.
- Score: 66.20146549150475
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Free energy perturbation (FEP) was proposed by Zwanzig more than six decades
ago as a method to estimate free energy differences, and has since inspired a
huge body of related methods that use it as an integral building block. Being
an importance sampling based estimator, however, FEP suffers from a severe
limitation: the requirement of sufficient overlap between distributions. One
strategy to mitigate this problem, called Targeted Free Energy Perturbation,
uses a high-dimensional mapping in configuration space to increase overlap of
the underlying distributions. Despite its potential, this method has attracted
only limited attention due to the formidable challenge of formulating a
tractable mapping. Here, we cast Targeted FEP as a machine learning problem in
which the mapping is parameterized as a neural network that is optimized so as
to increase overlap. We develop a new model architecture that respects
permutational and periodic symmetries often encountered in atomistic
simulations and test our method on a fully-periodic solvation system. We
demonstrate that our method leads to a substantial variance reduction in free
energy estimates when compared against baselines, without requiring any
additional data.
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