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¹H NMR: Long-Range Coupling01:27

¹H NMR: Long-Range Coupling

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The coupling interactions of nuclei across four or more bonds are usually weak, with J values less than 1 Hz. While these are usually not observed in spectra, the presence of multiple bonds along the coupling pathway can result in observable long-range coupling.
In alkenes, spin information is communicated via σ–π overlap, as seen in allylic (four-bond) and homoallylic (five-bond) couplings. These coupling interactions are stronger when the σ bond is parallel to the alkene...
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The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Neuronal Communication01:28

Neuronal Communication

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Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
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Action Potential01:31

Action Potential

7.8K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they...
7.8K
Interpreting ¹H NMR Signal Splitting: The (n + 1) Rule01:10

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

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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...
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MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

10.3K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
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Related Experiment Video

Updated: Jun 4, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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How graph neural network interatomic potentials extrapolate: Role of the message-passing algorithm.

Sungwoo Kang1

  • 1Computational Science Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.

The Journal of Chemical Physics
|December 23, 2024
PubMed
Summary
This summary is machine-generated.

Graph neural network interatomic potentials (GNN-IPs) learn non-local electrostatic interactions, explaining their ability to extrapolate to new material structures. This capability is crucial for accurate predictions in diverse, untrained domains.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Graph neural network interatomic potentials (GNN-IPs) show promise for materials modeling.
  • Universal GNN-IPs trained on crystalline data often extrapolate well to surfaces and amorphous structures.
  • The theoretical basis for this extrapolation capability remains unclear.

Purpose of the Study:

  • To provide a theoretical explanation for the extrapolation capabilities of GNN-IPs.
  • To investigate how GNN-IPs learn and predict interactions in untrained geometries.
  • To identify factors influencing the extrapolation performance of universal potentials.

Main Methods:

  • Demonstrated GNN-IPs' ability to capture non-local electrostatic interactions via message-passing.
  • Tested GNN-IP models (SevenNet, MACE) on toy models and DFT data for electrostatic force prediction.
  • Analyzed the extrapolation of Coulomb and kinetic energy terms in universal GNN-IPs.
  • Investigated the impact of hyperparameters on extrapolation performance.

Main Results:

  • GNN-IPs accurately predict electrostatic forces in untrained domains, learning the Coulomb interaction's functional form.
  • The ability to learn non-local electrostatics, combined with GNN embedding, explains extrapolation.
  • SevenNet-0 successfully extrapolates Coulomb interactions but not kinetic energy forces in untrained domains.
  • Hyperparameters significantly impact extrapolation performance, with specific limitations identified.

Conclusions:

  • GNN-IPs' extrapolation ability stems from learning non-local electrostatic interactions.
  • The embedding nature of GNNs further contributes to this phenomenon.
  • Understanding these mechanisms is key to developing more robust universal interatomic potentials.