Python-based MicroKinetic Modelling Kit

Within ab initio microkinetic modeling, chemokinetic networks are constructed using electronic structure calculations, typically periodic density functional theory (DFT). To date, there is no standardized format for storing the research data underlying these models. As a result, authors in the field employ a wide range of approaches, ranging from large tables of rate constants at fixed temperatures accompanied by a few renderings of geometric structures, to highly detailed datasets with interactive scripts that allow input files to be regenerated under different process conditions.

The purpose of this toolkit is to provide a reproducible and, hopefully, facile procedure for collecting and storing microkinetic research data. Here, we focus on the use case most commonly employed in our research group: the collection of kinetic parameters from plane-wave DFT calculations performed with VASP. In constructing the microkinetic model, energies and vibrational frequencies are extracted from the DFT calculations and used, via standard statistical thermodynamics approaches, to derive the input parameters underlying reaction rate constants—namely, activation energies and pre-exponential factors for elementary reaction steps.

All data are stored in YAML format, ensuring cross-platform interoperability while retaining a high degree of human readability. In addition to storing information for individual thermodynamic states (namely initial, transition, and final states) we define a unified YAML schema that encapsulates the complete microkinetic network by referencing these states. Specifically, each thermodynamic state is represented by a dedicated YAML file containing the electronic energy, vibrational frequency data, structural geometry, and calculation parameters. An overarching YAML file then references these state files and defines the microkinetic network through the specification of the elementary reaction steps.

Clone this repository and install using. It is recommended to use PyMKMKit in a virtual environment.

pip install .

To test that the kit is working correctly, one can run:

python -m pytest -s