Path integral Monte Carlo with importance sampling for excitons interacting with an arbitrary phonon bath
Sangwoo Shim, Al\'an Aspuru-Guzik

TL;DR
This paper develops an efficient path integral Monte Carlo method with importance sampling for studying excitons interacting with arbitrary phonon baths, enabling accurate simulations of their quantum dynamics at finite temperatures.
Contribution
It introduces a population-normalized estimator and an approximated gradient for importance sampling, improving computational efficiency and applicability to complex environments.
Findings
Validated on a 1D model system showing correct temperature dependence.
Demonstrated applicability to anharmonic environments like multichromophoric systems.
Achieved numerically exact results for exciton coherence and populations.
Abstract
The reduced density matrix of excitons coupled to a phonon bath at a finite temperature is studied using the path integral Monte Carlo method. Appropriate choices of estimators and importance sampling schemes are crucial to the performance of the Monte Carlo simulation. We show that by choosing the population-normalized estimator for the reduced density matrix, an efficient and physically-meaningful sampling function can be obtained. In addition, the nonadiabatic phonon probability density is obtained as a byproduct during the sampling procedure. For importance sampling, we adopted the Metropolis-adjusted Langevin algorithm. The analytic expression for the gradient of the target probability density function associated with the population-normalized estimator cannot be obtained in closed form without a matrix power series. An approximated gradient that can be efficiently calculated is…
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