STable AutoCorrelation Integral Estimator: Robust and Accurate Transport Properties from Molecular Dynamics Simulations

G Toraman and D Fauconnier and T Verstraelen, JOURNAL OF CHEMICAL INFORMATION AND MODELING, 65, 10445-10464 (2025).

DOI: 10.1021/acs.jcim.5c01475

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 data set to rigorously validate STACIE, comprising 15,360 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.

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