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Calculations of Electric Potential II01:27

Calculations of Electric Potential II

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An electric dipole is a system of two equal but opposite charges, separated by a fixed distance. This system is used to model many real-world systems, including atomic and molecular interactions. One of these systems is the water molecule, but only under certain circumstances. These circumstances are met inside a microwave oven, where electric fields with alternating directions make the water molecules change orientation. This vibration is equivalent to heat at the molecular level.
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Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
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A permanent electric dipole orients itself along an external electric field. This rotation can be quantified by defining the potential energy because the external torque does work in rotating it. Then, the potential energy is minimum at the parallel configuration and maximum at the antiparallel configuration. While the former is a stable equilibrium, the latter is an unstable equilibrium.
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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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Force can be calculated from the expression for potential energy, which is a function of position. The component of a conservative force, in a particular direction, equals the negative of the derivative of the corresponding potential energy with respect to the displacement in that direction. For regions where potential energy changes rapidly with displacement, the work done and force is maximum. Also, when force is applied along the positive coordinate axis, the potential energy decreases with...
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Updated: Sep 28, 2025

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A deep potential model with long-range electrostatic interactions.

Linfeng Zhang1, Han Wang2, Maria Carolina Muniz3

  • 1DP Technology, Beijing, People's Republic of China.

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|April 2, 2022
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Summary
This summary is machine-generated.

This study introduces the Deep Potential Long-Range (DPLR) model, enhancing machine learning for molecular simulations by incorporating explicit long-range electrostatic interactions for improved accuracy and predictive power in complex systems.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Deep Potential (DP) models enable accurate molecular simulations at near-empirical force field costs.
  • Existing DP models lack explicit long-range interactions, limiting their ability to describe Coulombic effects.
  • This deficiency hinders the accurate simulation of properties dependent on long-range forces.

Purpose of the Study:

  • To extend the Deep Potential model by incorporating long-range electrostatic interactions.
  • To develop a model that accurately describes properties derived from the Coulombic tail of forces.
  • To improve the accuracy and predictive power of machine learning potentials for multi-atomic systems.

Main Methods:

  • Approximating long-range electrostatic interactions using spherical Gaussian charge distributions at ionic and electronic sites.
  • Defining charge distributions based on maximally localized Wannier centers, modeled by deep neural networks.
  • Integrating electrostatic energy into the existing DP framework to create the Deep Potential Long-Range (DPLR) model.

Main Results:

  • The DPLR model yields a smooth potential energy surface with analytical forces and virial.
  • The model recovers missing effects from the standard DP scheme, enhancing accuracy.
  • DPLR successfully extrapolates potential energy surfaces learned from small systems to larger ones.

Conclusions:

  • The DPLR model effectively incorporates long-range electrostatics, overcoming limitations of standard DP models.
  • This advancement improves the accuracy and predictive capabilities for molecular simulations.
  • Demonstrated utility in simulating water dimer, water-slab interactions, and NaCl phonon dispersion.