Journal of Chemical Information and Modeling· 2026Q1
ThermoLIB─A Python Library for Constructing and Post-Processing Free-Energy Surfaces to Extract Thermodynamic and Kinetic Properties
- 1citations
- Q1SCImago
- 2026year
Short summary
ThermoLIB is a new Python/Cython library that constructs free-energy surfaces from molecular simulations, estimates errors using maximum-likelihood methods, and extracts thermodynamic/kinetic properties like rate constants.
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Key points
- ThermoLIB constructs free-energy surfaces from molecular simulation data.
- It employs maximum-likelihood estimators for robust error estimation.
- The library enables transformation of free-energy surfaces to different collective variable spaces.
- ThermoLIB extracts CV-independent thermodynamic and kinetic properties, such as rate constants with uncertainty estimates.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Journal of Chemical Information and Modeling, 2026 · DOI ↗
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Field: Materials Chemistry
Materials ChemistryMaterials Science