Energy landscapes are a powerful tool for describing the kinetics of complex systems at the atomic scale. However, because of the large disparity of time scales of kinetic processes, it can be difficult to calculate the evolution of properties over long (e.g., experimental) time scales. KineticPy is a Python-based program designed to calculate the evolution of occupational probabilities of every basin in an energy landscape (or enthalpy landscape), taking advantage of the disparate time scales of the system to accelerate the calculations utilizing the framework of broken ergodicity. Given the relevant parameters of the energy landscape (i.e., the set of inherent structures, transition points, and corresponding property values) and a desired temperature path, KineticPy calculates the kinetics of the landscape on any arbitrary time scale. KineticPy offers a unified platform for exploring energy landscapes and calculating the evolution of property values for any system of interest. KineticPy is open source and structured in a modular fashion to facilitate future extensions to the code.
All Science Journal Classification (ASJC) codes
- Computer Science Applications