STable AutoCorrelation Integral Estimator (STACIE): Robust and accurate transport properties from molecular dynamics simulations

TL;DR

STACIE is a new Python tool that accurately estimates autocorrelation integrals from molecular dynamics data, providing reliable transport property calculations without manual hyperparameter tuning, validated on extensive synthetic benchmarks.

Contribution

It introduces a robust, uncertainty-aware algorithm for autocorrelation integral estimation that requires no manual hyperparameter adjustment, applicable across scientific fields.

Findings

Accurately estimates ionic conductivity in electrolyte solutions.

Validated on a large synthetic benchmark dataset.

Open source implementation available on GitHub and PyPI.

Abstract

STACIE (STable AutoCorrelation Integral Estimator) is a novel algorithm and Python package that delivers robust, uncertainty-aware estimates of autocorrelation integrals from time-correlated data. While its primary application is deriving transport properties from equilibrium molecular dynamics simulations, STACIE is equally applicable to time-correlated data in other scientific fields. A key feature of STACIE is its ability to provide robust and accurate estimates without requiring manual adjustment of hyperparameters. Additionally, one can follow a simple protocol to prepare sufficient simulation data to achieve a desired relative error of the transport property. We demonstrate its application by estimating the ionic electrical conductivity of a NaCl-water electrolyte solution. We also present a massive synthetic benchmark dataset to rigorously validate STACIE, comprising 15360 sets of time-correlated inputs generated with diverse covariance kernels with known autocorrelation integrals. STACIE is open source and available on GitHub and PyPI, with comprehensive documentation and examples.