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

TL;DR

ThermoLIB is a Python/Cython library that facilitates constructing, transforming, and analyzing free energy surfaces from molecular simulation data, enabling extraction of thermodynamic and kinetic properties with error estimation.

Contribution

It introduces a comprehensive, open-source tool for post-processing free energy surfaces, including transformations, error analysis, and property extraction, based on maximum likelihood estimation.

Findings

Supports transformation between collective variables

Provides error bars and covariance matrices

Enables extraction of thermodynamic and kinetic properties

Abstract

ThermoLIB is Python/Cython library designed to be used as a post-processing tool for constructing free energy surfaces 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 and includes error bars on and full covariance matrix between all points on the free energy surface using the Fisher information matrix. The free energy surfaces can be transformed a posteriori to other collective variables, projected towards lower dimensional CV-spaces and even deprojected towards higher dimensional CV-spaces if additional information from the simulation is provided in the form of a conditional probability. Finally, one can extract usefull thermodynamic and kinetic properties such as the reaction free energy and kinetic rate constant. Error bars on the free energy surfaces are propagated throughout al these operations. We briefly illustrate the capabilities of ThermoLIB by means of some tutorials and case studies.