Spectral denoising for unsupervised analysis of correlated ionic transport

TL;DR

This paper introduces a spectral decomposition method to efficiently analyze correlated ionic transport from molecular dynamics, reducing uncertainty and computation time in conductivity calculations for electrolyte systems.

Contribution

The paper presents a novel spectral denoising approach that captures correlation structures in ionic trajectories, improving efficiency and robustness over traditional methods.

Findings

Reduces uncertainty in ionic conductivity calculations

Accelerates computation of transport properties

Demonstrates effectiveness on realistic electrolyte materials

Abstract

Computation of correlated ionic transport properties from molecular dynamics in the Green-Kubo formalism is expensive as one cannot rely on the affordable mean square displacement approach. We use spectral decomposition of the short-time ionic displacement covariance to learn a set of diffusion eigenmodes that encode the correlation structure and form a basis for analyzing the ionic trajectories. This allows to systematically reduce the uncertainty and accelerate computations of ionic conductivity in systems with a steady-state correlation structure. We provide mathematical and numerical proofs of the method's robustness, and demonstrate it on realistic electrolyte materials.