How to improve the accuracy of semiclassical and quasiclassical dynamics with and without generalized quantum master equations

TL;DR

This paper investigates how short-time analysis and memory kernel cutoff protocols can enhance the accuracy of semiclassical and quasiclassical dynamics, especially when combined with generalized quantum master equations, addressing accuracy and unphysical behavior issues.

Contribution

It uncovers the mechanism behind accuracy improvements in SC-GQMEs and develops a protocol for determining memory kernel cutoffs in challenging regimes.

Findings

Short-time derivatives delay SC inaccuracy onset.

Numerical integration of derivatives improves short-time accuracy.

Memory kernel cutoff protocol avoids unphysical dynamics.

Abstract

Semi- and quasi-classical (SC) theories can handle arbitrary interatomic interactions and are thus well-suited to predict quantum dynamics in condensed phases that encode energy and charge transport, spectroscopic responses, and chemical reactivity. However, SC theories can be computationally expensive and inaccurate. When combined with generalized quantum master equations (GQMEs), the resulting SC-GQMEs have been observed to enhance the efficiency and accuracy of SC dynamics. Yet, while the mechanism responsible for improved efficiency is clear, the underlying improved accuracy remains elusive. What is worse, SC-GQMEs can yield unphysical dynamics in challenging parameter regimes -- a shortcoming that might be avoided if the mechanism of accuracy improvement were understood. Here, we uncover this mechanism. We leverage short-time analyses to prove that exact, "left-handed" time-derivatives delay the onset of SC inaccuracy, and show that their numerical integration yields dynamics with improved accuracy, even without the GQME. We find, however, that these derivatives are a double-edged sword: while offering greater short-time accuracy, they become unphysical in challenging parameter regimes. Because short-lived memory kernels can leverage short-time accuracy while circumventing long-time instability, we develop a protocol to unambiguously determine the memory kernel cutoff, even in challenging regimes where previous treatments had failed. Our insights into accuracy improvement and kernel cutoff protocol can be expected to apply to complex systems that go beyond simple models.