ThermoLIB─A Python Library for Constructing and Post-Processing Free-Energy Surfaces to Extract Thermodynamic and Kinetic Properties

Abstract

Abstract ThermoLIB is a Python/Cython library designed to be used as a postprocessing tool for constructing free-energy surfaces, including adequate error estimation from the output of molecular simulations, transforming them between different collective variables (CVs), and extracting thermodynamic and kinetic information. ThermoLIB is available for download on GitHub and comes with extended documentation as well as many tutorials. The implementation is based on the theory of maximum-likelihood estimators for robust error estimation. The free-energy surfaces can be transformed or projected a posteriori to a larger or lower CV space by constructing conditional probabilities from the simulation results. Finally, useful CV-independent thermodynamic and kinetic properties, such as the rate constant, can be readily obtained, together with their uncertainty estimates. We briefly illustrate the capabilities of ThermoLIB by means of tutorials and case studies.