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Potential energy or potential function plays an essential role in determining the stability of a mechanical system. If a system is subjected to both gravitational and elastic forces, the potential function of the system can be expressed as the algebraic sum of gravitational and elastic potential energy. If the system is in equilibrium and is displaced by a small amount, then the work done on the system equals the negative of the change in the system's potential energy from the initial to...
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Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
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The effective concentration of a species in a solution can be expressed precisely in terms of its activity. Activity considers the effect of electrolytes present in the vicinity of the species of interest and depends on the ionic strength of the solution. The activity of a species is expressed as the product of molar concentration and the activity coefficient of the species.
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Related Experiment Video

Updated: Jun 28, 2025

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
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Assessing Permutationally Invariant Polynomial and Symmetric Gradient Domain Machine Learning Potential Energy

Priyanka Pandey1, Mrinal Arandhara2, Paul L Houston3,4

  • 1Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States.

The Journal of Physical Chemistry. A
|April 16, 2024
PubMed
Summary
This summary is machine-generated.

Two machine-learned potentials for the singly hydrated hydroxide anion OH-(H2O) were compared. The permutationally invariant polynomial (PIP) potential energy surface (PES) is significantly faster than the symmetric gradient domain machine learning (sGDML) PES.

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Area of Science:

  • Computational chemistry
  • Molecular dynamics
  • Quantum chemistry

Background:

  • The singly hydrated hydroxide anion, OH-(H2O), is crucial for understanding water's molecular behavior.
  • Accurate potential energy surfaces (PESs) are essential for molecular simulations of this system.

Purpose of the Study:

  • To develop and compare two machine-learned PESs for OH-(H2O): one using symmetric gradient domain machine learning (sGDML) and another based on permutationally invariant polynomials (PIPs).
  • To evaluate the precision, properties, and computational speed of both machine-learned potentials.

Main Methods:

  • Fitting machine-learned potentials using sGDML and PIP methods.
  • Comparing the accuracy and performance of the developed PESs.
  • Conducting Diffusion Monte Carlo calculations to determine vibrational ground states.

Main Results:

  • Both sGDML and PIP potentials demonstrated similar precision in describing the OH-(H2O) system.
  • The PIP PES exhibited significantly faster energy and gradient evaluations compared to the sGDML PES.
  • Diffusion Monte Carlo calculations using both potentials showed comparable large anharmonic downshifts in zero-point energy.

Conclusions:

  • The PIP potential energy surface offers a computationally efficient alternative to the sGDML potential for studying the singly hydrated hydroxide anion.
  • Despite speed differences, both potentials accurately capture key anharmonic vibrational properties.