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Related Concept Videos

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Graded Potential

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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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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 propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
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The energy stored by a structure and location of matter in space is called potential energy. For instance, raising a kettlebell changes its spatial location and increases its potential energy. Similarly, a stretched rubber band contains potential energy which, under certain conditions, can be converted into other forms of energy, such as kinetic energy.
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

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Deep coarse-grained potentials via relative entropy minimization.

Stephan Thaler1, Maximilian Stupp1, Julija Zavadlav1

  • 1Multiscale Modeling of Fluid Materials, Department of Engineering Physics and Computation, TUM School of Engineering and Design, Technical University of Munich, Munich, Germany.

The Journal of Chemical Physics
|December 31, 2022
PubMed
Summary
This summary is machine-generated.

Relative entropy (RE) training improves coarse-grained neural network (NN) potentials, offering better data efficiency and accuracy than force matching. This method enhances free energy surfaces and molecular dynamics simulations.

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Area of Science:

  • Computational chemistry
  • Materials science
  • Statistical mechanics

Background:

  • Neural network (NN) potentials are suitable for coarse-grained (CG) models, approximating potentials of mean force for accurate simulations.
  • Force matching (FM) training for CG NN potentials is limited by finite data effects, requiring prior potentials and showing sensitivity to transition region errors.

Purpose of the Study:

  • To investigate the application and benefits of relative entropy (RE) minimization for training NN potentials in CG models.
  • To compare RE training with traditional FM training for CG NN potentials.

Main Methods:

  • Applied RE minimization to train NN potentials for CG models, a method not previously used for NN potentials.
  • Benchmarked RE training against FM training using liquid water and alanine dipeptide systems.

Main Results:

  • RE training demonstrated superior data efficiency by accessing the CG distribution, leading to improved free energy surfaces.
  • RE-trained potentials showed reduced sensitivity to prior potentials and corrected time integration errors, enabling larger simulation time steps.
  • Enhanced accuracy and reliability in CG molecular dynamics simulations were observed with RE training.

Conclusions:

  • Relative entropy minimization is a more effective training objective for CG NN potentials compared to force matching.
  • RE training enhances the accuracy, reliability, and data efficiency of CG NN potentials, paving the way for more robust simulations.