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arXiv · 2007.08734

Spectral denoising for unsupervised analysis of correlated ionic transport

Nicola Molinari·Yu Xie·Ian Leifer·Aris Marcolongo·Mordechai Kornbluth·Boris Kozinsky
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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.

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Nicola Molinari, Yu Xie, Ian Leifer, Aris Marcolongo, Mordechai Kornbluth, Boris Kozinsky. 2021-06-16. Spectral denoising for unsupervised analysis of correlated ionic transport. https://doi.org/10.1103/physrevlett.127.025901

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