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Quantum Numbers02:43

Quantum Numbers

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It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
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Neural Circuits

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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...
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The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing hydrogen spectra.
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Inductive Effects on Chemical Shift: Overview01:27

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The protons in unsubstituted alkanes are strongly shielded with chemical shifts below 1.8 ppm. Methine, methylene, and methyl protons appear at approximately 1.7, 1.2 and 0.7 ppm, while the proton signal from methane appears at 0.23 ppm. An electronegative substituent, such as chlorine, withdraws the electron density from the protons, increasing their chemical shift. Progressive substitution of the hydrogens in methane by chlorine shifts the proton signals increasingly downfield, to 3.05 ppm in...
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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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Related Experiment Video

Updated: Mar 9, 2026

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

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Quantum-chemical insights from deep tensor neural networks.

Kristof T Schütt1, Farhad Arbabzadah1, Stefan Chmiela1

  • 1Machine Learning Group, Technische Universität Berlin, Marchstr. 23, 10587 Berlin, Germany.

Nature Communications
|January 10, 2017
PubMed
Summary

Machine learning now offers deep insights into quantum many-body systems. Our deep tensor neural networks provide accurate predictions for molecular properties, advancing quantum chemistry.

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Last Updated: Mar 9, 2026

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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Area of Science:

  • Quantum Chemistry
  • Machine Learning
  • Computational Physics

Background:

  • Machine learning has revolutionized many scientific fields.
  • Understanding complex quantum many-body systems remains a significant challenge.
  • Developing accurate predictive models for molecular behavior is crucial.

Purpose of the Study:

  • To develop an efficient deep learning approach for analyzing quantum many-body systems.
  • To achieve spatially and chemically resolved insights into quantum-mechanical observables.
  • To demonstrate the potential of machine learning in uncovering complex quantum-chemical phenomena.

Main Methods:

  • Unification of many-body Hamiltonians with purpose-designed deep tensor neural networks.
  • Development of a deep learning model capable of size-extensive predictions.
  • Training and validation on molecular systems of intermediate size.

Main Results:

  • Uniformly accurate (1 kcal mol⁻¹) predictions in compositional and configurational chemical space.
  • Demonstration of size-extensive and accurate predictions for molecular properties.
  • Identification of a chemical classification of aromatic rings based on stability.

Conclusions:

  • Deep learning offers a powerful new avenue for understanding quantum many-body systems.
  • The developed deep tensor neural network approach provides reliable predictions for molecular energies and properties.
  • This methodology has broad applications in predicting atomic energies, chemical potentials, isomer energies, and peculiar electronic structures, highlighting machine learning's potential in quantum chemistry.