KBKit: A Python Toolkit for Kirkwood-Buff Theory from Molecular Dynamics

Thermodynamic properties of liquid mixtures govern processes that range from drug delivery to energy storage, yet extracting these properties from molecular simulations remains challenging. Kirkwood-Buff (KB) theory offers a rigorous route by linking microscopic pair distribution functions to macroscopic free energies, but its practical application has been hindered by the specialized corrections required to translate finite-volume simulation data to the thermodynamic limit. KBKit is an open-source Python package that addresses this challenge by applying well-established finite-volume corrections to radial distribution functions (RDFs) obtained from molecular dynamics simulations, following the procedure outlined by Simon, J.-M. . ( J. Chem. Phys. 2022, 157, 130901). Three corrections are applied sequentially: the Ganguly, P.; van der Vegt, N. F. A.( J. Chem. Theory Comput. 2013, 9, 1347-1355) correction for finite particle number effects on the RDF, the Krüger, P. ( J. Phys. Chem. Lett. 2013, 4, 235-238) correction for finite-volume effects, and the Dawass, N. ( Nanomaterials 2020, 10, 771) extrapolation to the thermodynamic limit. Built-in convergence diagnostics alert users when Kirkwood-Buff integrals (KBIs) fail to meet established quality criteria. Using input from standard GROMACS output files, KBKit automatically computes KBIs and derived thermodynamic quantities, including: activity coefficients, excess Gibbs energy, and zero-wavevector structure factors, for both multicomponent and electrolyte systems. Written with modern software-engineering practices such as continuous integration, extensive unit testing, and thorough documentation, KBKit is both reliable and easy to extend. By condensing the complex KBI correction and analysis workflow into a few intuitive commands, KBKit enables researchers to incorporate KB theory into routine simulation workflows and accelerate the characterization of solution-phase thermodynamics.