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Published June 26, 2025 | Version 1.0.0
Dataset Open

The AutoCorrelation Integral Drill (ACID) Test Set

  • 1. ROR icon Ghent University
  • 2. Labo Soete, Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Ghent, Belgium
  • 3. FlandersMake\@UGent, Core Lab EEDT-MP, 3001 Leuven, Belgium
  • 4. Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Zwijnaarde, Belgium

Description

This repository contains the scripts and StepUp workflows to regenerate the "AutoCorrelation Integral Drill" (ACID) test set. The ACID test set comprises a diverse collection of algorithmically generated time series designed to evaluate the performance of algorithms that compute the autocorrelation integral. The set contains in total 15360 test cases, and each case consists of one or more time series. The cases differ in the kernel characterizing the time correlations, the number of time series, and the length of the time series. For each combination of kernel, number of sequences and sequence length, 64 test cases are generated with different random seeds to allow for a systematic validation of uncertainty estimates. The total dataset, once generated, is about 80 GB in size.

In addition to the ACID test set, this repository also contains scripts and workflows to validate STACIE, a software package for the computation of the autocorrelation integral. The results of this analysis are discussed in the following paper:

Gözdenur, T.; Fauconnier, D.; Verstraelen, T. "STable AutoCorrelation Integral Estimator (STACIE): Robust and accurate transport properties from molecular dynamics simulations" Journal of Chemical Information and Modeling Article ASAP, 2025, doi:10.1021/acs.jcim.5c01475, arXiv:2506.20438.

Files

acid-dataset.pdf

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Additional details

Related works

Cites
Software documentation: https://molmod.github.io/stacie (URL)
Compiles
Dataset: https://github.com/molmod/acid (URL)
Is described by
Preprint: arXiv:2506.20438 (arXiv)
Journal article: 10.1021/acs.jcim.5c01475 (DOI)
Requires
Software: https://github.com/molmod/stacie (URL)

Funding

Ghent University
Molecular Dynamics Modelling of Lubricants at Ultra-High Pressures with Force Fields derived Ab Initio BOF/24J/2021/118