ReaxKit: A Modular Python Toolkit for Preparing, Parsing, and Analyzing ReaxFF Molecular Dynamics Simulations
Abstract
Empirical reactive force field (RFF) molecular dynamics enables atomistic simulation of bond breaking, bond formation, charge redistribution, and structural evolution in chemically complex systems. The ReaxFF method is arguably the most popular and transferable of the currently available RFF methods. However, routine use of ReaxFF often requires substantial manual effort to prepare inputs, interpret engine-specific outputs, organize simulation artifacts, and develop custom analysis scripts, limiting reproducibility and scalability. Here, we present ReaxKit, a modular Python toolkit for preparing, parsing, analyzing, and managing ReaxFF molecular dynamics simulations. ReaxKit uses a separation-of-concerns architecture that distinguishes engine-specific input/output handling, canonical domain data models, scientific analysis, workflow orchestration, presentation, storage, and graphical interaction. Engine adapters convert outputs from supported simulation environments into typed, engine-independent data structures, allowing analysis modules to operate independently of native file formats. User requests are executed through consistent command-line, functional Python, and browser-based graphical interfaces, while a dedicated workspace preserves raw data, normalized datasets, analysis settings, results, logs, caches, and provenance information. Representative applications demonstrate the breadth of the toolkit, including automated generation of elastic and equation-of-state training data from Materials Project structures and mechanical properties, characterization of active sites and local structural environments, and execution of simulation campaigns through YAML-defined study workflows. These capabilities show that ReaxKit supports all essential stages of the ReaxFF workflow.
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BibTeX
@misc{dinani2026reaxkit,
title = {{ReaxKit: A Modular Python Toolkit for Preparing, Parsing, and Analyzing ReaxFF Molecular Dynamics Simulations}},
author = {Ali Mohammadi Dinani and Alireza Sepehrinezhad and Anirban Phukan and Asma Ul Hosna and Jupjeet Dhingra and Mozhdeh Mirakhory and Seyed Mahmoud Mortazavi and Yun Kyung Shin and Swarit Dwivedi and Adri C. T. van Duin},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.22019},
eprint = {2609.22019},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.22019},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - ReaxKit: A Modular Python Toolkit for Preparing, Parsing, and Analyzing ReaxFF Molecular Dynamics Simulations
AU - Ali Mohammadi Dinani
AU - Alireza Sepehrinezhad
AU - Anirban Phukan
AU - Asma Ul Hosna
AU - Jupjeet Dhingra
AU - Mozhdeh Mirakhory
AU - Seyed Mahmoud Mortazavi
AU - Yun Kyung Shin
AU - Swarit Dwivedi
AU - Adri C. T. van Duin
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.22019
UR - https://arxiv.org/abs/2609.22019
AB - Empirical reactive force field (RFF) molecular dynamics enables atomistic simulation of bond breaking, bond formation, charge redistribution, and structural evolution in chemically complex systems. The ReaxFF method is arguably the most popular and transferable of the currently available RFF methods. However, routine use of ReaxFF often requires substantial manual effort to prepare inputs, interpret engine-specific outputs, organize simulation artifacts, and develop custom analysis scripts, limiting reproducibility and scalability. Here, we present ReaxKit, a modular Python toolkit for preparing, parsing, analyzing, and managing ReaxFF molecular dynamics simulations. ReaxKit uses a separation-of-concerns architecture that distinguishes engine-specific input/output handling, canonical domain data models, scientific analysis, workflow orchestration, presentation, storage, and graphical interaction. Engine adapters convert outputs from supported simulation environments into typed, engine-independent data structures, allowing analysis modules to operate independently of native file formats. User requests are executed through consistent command-line, functional Python, and browser-based graphical interfaces, while a dedicated workspace preserves raw data, normalized datasets, analysis settings, results, logs, caches, and provenance information. Representative applications demonstrate the breadth of the toolkit, including automated generation of elastic and equation-of-state training data from Materials Project structures and mechanical properties, characterization of active sites and local structural environments, and execution of simulation campaigns through YAML-defined study workflows. These capabilities show that ReaxKit supports all essential stages of the ReaxFF workflow.
ER -