Result: simba: a python-based single-point astrochemical solver and analysis tool Open Access.

Determining molecular abundances in astrophysical environments is crucial for interpreting observational data and constraining physical conditions in these regions. Chemical modelling tools are essential for simulating the complex processes that govern molecular evolution. We present simba ('Solver for Inferring Molecular aBundances in Astrophysical environments'), a new python -based single-point astrochemical modelling package designed to solve chemical reaction networks across diverse astrophysical environments. The software follows standardized rate equation approaches to evolve molecular abundances under specified physical conditions, incorporating gas-phase chemistry, grain-surface processes, and photochemistry. While leveraging python for accessibility, performance-critical routines utilize just-in-time compilation to achieve computational efficiency suitable for research applications. A key feature of simba is its graphical interface, which enables rapid investigation of chemical evolution under varying physical conditions. This makes it particularly valuable for exploring parameter dependencies and complementing more computationally intensive multidimensional models. We demonstrate the package's capabilities by modelling chemical evolution in a photoevaporative flow driven by external far-ultraviolet irradiation. Using simplified gas dynamics, we chain multiple simba instances to create a dynamic 1D model where gas evolves both chemically and dynamically. Comparing this approach to typical 'static' models – where chemistry in each grid cell evolves independently – reveals that molecular ices, especially those with relatively high binding energies like H |$_2$| O, can survive much farther into the flow than static models predict. This example case highlights how simba can be extended to higher dimensions for investigating complex chemical processes. The package is open-source and includes comprehensive documentation. [ABSTRACT FROM AUTHOR]

Copyright of Monthly Notices of the Royal Astronomical Society is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)