ArticleJournal of chemical information and modeling2025
STable AutoCorrelation Integral Estimator: Robust and Accurate Transport Properties from Molecular Dynamics Simulations.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MDTransport: A Modular, Open-Source, Extensible Python Tool for Computing Transport Properties from GROMACS and LAMMPS Simulations.Journal of chemical information and modeling · 2026Article
- Lessons Learned on Obtaining Reliable Conductivity Estimates From Molecular Dynamics Simulations.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- CRISP: Enhancing ASE Workflows With Advanced Molecular Simulation Post-Processing.Journal of computational chemistry · 2026Article
- STable AutoCorrelation Integral Estimator: Robust and Accurate Transport Properties from Molecular Dynamics Simulations.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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 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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Registered trials
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