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The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
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How can we compare the energy that releases from one reaction to that of another reaction? We use a measurement of free energy to quantitate these energy transfers. Scientists call this free energy Gibbs free energy (abbreviated with the letter G) after Josiah Willard Gibbs, the scientist who developed the measurement. According to the second law of thermodynamics, all energy transfers involve losing some energy in an unusable form such as heat, resulting in entropy. Gibbs free energy...
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Molecular Understanding of Free-Energy Landscapes.

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Summary

Free-energy surfaces (FESs) provide a clear view of molecular behavior and stability. This review guides researchers in using FESs and advanced machine learning for better molecular simulations.

Keywords:
artificial intelligencecomputational physical chemistryfree-energy surfacesmachine learningmolecular thermodynamicsreaction coordinates

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Area of Science:

  • Computational chemistry
  • Statistical mechanics
  • Chemical engineering

Background:

  • Free-energy surfaces (FESs) are essential for understanding molecular structures, transformations, and stability.
  • FESs simplify complex atomistic simulations into interpretable landscapes.
  • They bridge molecular details with macroscopic properties.

Purpose of the Study:

  • To provide a guide for computing and interpreting FESs.
  • To cover statistical-mechanical foundations and modern machine learning approaches.
  • To aid researchers in molecular simulations, computational physical chemistry, and chemical engineering.

Main Methods:

  • Review of statistical-mechanical principles for FES computation.
  • Exploration of modern machine learning techniques for enhanced sampling.
  • Discussion of methods for FES representation and analysis.

Main Results:

  • FESs offer a unifying framework for molecular system analysis.
  • Machine learning significantly advances the sampling, representation, and analysis of FESs.
  • The review provides practical guidance for researchers.

Conclusions:

  • FESs are crucial for understanding molecular thermodynamics and kinetics.
  • Integrating machine learning with FES computation enhances simulation efficiency and insight.
  • This work serves as a comprehensive resource for computational scientists.