Population size bias in diffusion Monte Carlo

Population size bias in descendant-weighted diffusion quantum Monte Carlo simulations.

We consider the influence of population size on the accuracy of diffusion quantum Monte Carlo simulations that employ descendant weighting or forward walking techniques to compute expectation values of observables that do not commute with the Hamiltonian. We show that for a simple model system, the d-dimensional isotropic harmonic oscillator, the population size must increase rapidly with d in ...

Population Control Bias with Applications to Parallel Diffusion Monte Carlo

The future of scientific computing will be driven by highly distributed parallel machines with millions of compute nodes. In order to take advantage of this already arriving wave of computing capability we must identify and remove the remaining barriers to parallel scaling in the Diffusion Monte Carlo algorithm. To address these scaling issues in a simple way, we propose that a time delay be in...

Population Monte Carlo algorithms

We give a cross-disciplinary survey on “population” Monte Carlo algorithms. In these algorithms, a set of “walkers” or “particles” is used as a representation of a high-dimensional vector. The computation is carried out by a random walk and split/deletion of these objects. The algorithms are developed in various fields in physics and statistical sciences and called by lots of different terms – ...

Boltzmann bias grand canonical Monte Carlo.

We derive an efficient method for the insertion of structured particles in grand canonical Monte Carlo simulations of adsorption in very confining geometries. We extend this method to path integral simulations and use it to calculate the isotherm of adsorption of hydrogen isotopes in narrow carbon nanotubes (two-dimensional confinement) and slit pores (one-dimensional confinement) at the temper...

How to Optimize Conngurational Bias Monte Carlo?