The $\textit{u}$-series: A separable decomposition for electrostatics computation with improved accuracy

TL;DR

The paper introduces the u-series, a new Coulomb potential decomposition that enhances accuracy and reduces computational effort in electrostatics calculations for molecular dynamics, especially in periodic systems.

Contribution

The u-series provides a more accurate and computationally efficient Coulomb decomposition than the standard Ewald method, with potential for further performance gains on parallel supercomputers.

Findings

u-series achieves higher accuracy than Ewald decomposition at the same computational cost.

u-series reduces computational effort by approximately half compared to Ewald for the same accuracy.

Numerical demonstration on lipid membrane system confirms the effectiveness of the u-series.

Abstract

The evaluation of electrostatic energy for a set of point charges in a periodic lattice is a computationally expensive part of molecular dynamics simulations (and other applications) because of the long-range nature of the Coulomb interaction. A standard approach is to decompose the Coulomb potential into a near part, typically evaluated by direct summation up to a cutoff radius, and a far part, typically evaluated in Fourier space. In practice, all decomposition approaches involve approximations---such as cutting off the near-part direct sum---but it may be possible to find new decompositions with improved tradeoffs between accuracy and performance. Here we present the $\textit{u-series}$, a new decomposition of the Coulomb potential that is more accurate than the standard (Ewald) decomposition for a given amount of computational effort, and achieves the same accuracy as the Ewald decomposition with approximately half the computational effort. These improvements, which we demonstrate numerically using a lipid membrane system, arise because the $\textit{u}$-series is smooth on the entire real axis and exact up to the cutoff radius. Additional performance improvements over the Ewald decomposition may be possible in certain situations because the far part of the $\textit{u}$-series is a sum of Gaussians, and can thus be evaluated using algorithms that require a separable convolution kernel; we describe one such algorithm that reduces communication latency at the expense of communication bandwidth and computation, a tradeoff that may be advantageous on modern massively parallel supercomputers.