ZacrosTools: A Python Library for Automated Preparation, Analysis, and Visualization of Kinetic Monte Carlo Simulations with Zacros

This paper presents ZacrosTools, a free and open-source Python library designed to simplify and automate the preparation and analysis of kinetic Monte Carlo (KMC) simulations with the widely used Zacros package. ZacrosTools provides a user-friendly and robust interface for building KMC models, automating the generation of Zacros input files, extracting and processing simulation data, and visualizing results through multiple plotting functionalities. The library benefits new users by simplifying model preparation and helping them avoid common mistakes while also being suitable to advanced users who wish to fine-tune complex KMC models and conduct comprehensive analyses. ZacrosTools is extensively documented with numerous examples available on ReadTheDocs and is publicly accessible on GitHub under the MIT license. Furthermore, it integrates continuous integration via GitHub Actions to facilitate seamless contributions from the user community.

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Institutions

Publication Details

Journal
The Journal of Physical Chemistry A
Published
2025-07-14
DOI
https://doi.org/10.1021/acs.jpca.5c02802
Citations
2
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.04

Funders

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article

ZacrosTools: A Python Library for Automated Preparation, Analysis, and Visualization of Kinetic Monte Carlo Simulations with Zacros

Hèctor Prats
2 citations
The Journal of Physical Chemistry A
Machine Learning in Materials Science
1.04
article

ZacrosTools: A Python Library for Automated Preparation, Analysis, and Visualization of Kinetic Monte Carlo Simulations with Zacros

Hèctor Prats
article en
2 citations

Abstract

This paper presents ZacrosTools, a free and open-source Python library designed to simplify and automate the preparation and analysis of kinetic Monte Carlo (KMC) simulations with the widely used Zacros package. ZacrosTools provides a user-friendly and robust interface for building KMC models, automating the generation of Zacros input files, extracting and processing simulation data, and visualizing results through multiple plotting functionalities. The library benefits new users by simplifying model preparation and helping them avoid common mistakes while also being suitable to advanced users who wish to fine-tune complex KMC models and conduct comprehensive analyses. ZacrosTools is extensively documented with numerous examples available on ReadTheDocs and is publicly accessible on GitHub under the MIT license. Furthermore, it integrates continuous integration via GitHub Actions to facilitate seamless contributions from the user community.

The Journal of Physical Chemistry AVol. 129(29)
TU Wien (AT), University of Oxford (GB)
Leverhulme Trust, Austrian Science Fund
Openalex Percentile: Top 24%
Machine Learning in Materials Science
1.04
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