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Related Concept Videos

Calculations of Electric Potential II01:27

Calculations of Electric Potential II

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.
Consider a...
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule

In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the others.
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Electronic Structure of Atoms02:28

Electronic Structure of Atoms


An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum numbers:  n, l, ml, and...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Graded Potential01:19

Graded Potential

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.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or calcium...

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Related Experiment Videos

Construction of high-dimensional neural network potentials using environment-dependent atom pairs.

K V Jovan Jose1, Nongnuch Artrith, Jörg Behler

  • 1Lehrstuhl für Theoretische Chemie, Ruhr-Universität Bochum, D-44780 Bochum, Germany.

The Journal of Chemical Physics
|May 23, 2012
PubMed
Summary

Developing accurate interatomic potentials is key for chemical simulations. A new neural network (NN) method using atom pairs improves potential energy surface accuracy for molecular dynamics, offering a competitive alternative for complex systems.

Related Experiment Videos

Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Accurate potential energy determination is vital for chemical simulations but computationally expensive with electronic structure methods.
  • Developing efficient interatomic potentials for complex potential-energy surfaces remains challenging.
  • Feed-forward neural networks (NNs) offer a flexible approach to constructing accurate potential-energy surfaces (PESs).

Purpose of the Study:

  • To implement and evaluate a novel NN method based on atom pairs for constructing interatomic potentials.
  • To compare the accuracy and performance of the pair-based NN approach against the atom-based NN method.
  • To assess the suitability of the pair-based NN for complex systems in molecular dynamics (MD) simulations.

Main Methods:

  • Implementation of a feed-forward neural network (NN) utilizing atom pair information.
  • Comparative analysis of atom-based and pair-based NN potentials.
  • Validation using two distinct systems: methanol molecule and metallic copper.

Main Results:

  • Both atom-based and pair-based NN potentials accurately describe the potential energy surfaces.
  • The pair-based NN method demonstrated slightly higher accuracy compared to the atom-based approach.
  • The developed NN potentials are efficient for molecular dynamics (MD) simulations.

Conclusions:

  • The pair-based NN method provides a competitive and accurate alternative for constructing interatomic potentials.
  • This approach enhances the description of atomic interactions and chemical environments.
  • The method shows promise for simulating complex chemical systems with high fidelity.