Markov-chain sampling for long-range systems without evaluating the energy

TL;DR

This paper presents cell-veto algorithms enabling efficient Markov-chain Monte Carlo sampling of long-range interacting systems without energy evaluation, maintaining constant effort per move regardless of system size.

Contribution

It introduces a class of cell-veto algorithms that allow native, cutoff-free sampling of the Boltzmann distribution for long-range interactions, including Coulomb forces.

Findings

Effort per move remains constant as system size increases.

Algorithms successfully sample the Boltzmann distribution without energy evaluation.

Open-source Python implementations are provided.

Abstract

In past decades, enormous effort has been expended to develop algorithms and even to construct special-purpose computers in order to efficiently evaluate total energies and forces for long-range-interacting particle systems, with the particle-mesh Ewald and the fast multipole methods as well as the 'Anton' series of supercomputers serving as examples for biomolecular simulations. Cutoffs in the range of the interaction have also been used for large systems. All these methods require extrapolations. Within Markov-chain Monte Carlo, in thermal equilibrium, the Boltzmann distribution can however be sampled natively without evaluating the total interaction potential. Using as an example the Lennard-Jones interaction, we review past attempts in this direction, and then discuss in detail the class of cell-veto algorithms which make possible fast, native sampling of the Boltzmann distribution without any approximation, extrapolation, or cutoff even for the slowly decaying Coulomb interaction. The computing effort per move remains constant with increasing system size, as we show explicitly. We provide worked-out illustrations and pseudocode representations of the discussed algorithms. Python implementations are made available in an associated open-source software repository.