Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Electronic Structure of Atoms02:28

Electronic Structure of Atoms

27.2K

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...
27.2K
Molecular Models02:00

Molecular Models

42.9K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
42.9K
Molecular Shapes01:18

Molecular Shapes

60.2K
Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...
60.2K
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

55.3K
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.
55.3K
Atomic Structure01:17

Atomic Structure

15.5K
The Greek philosopher Democritus proposed that everything on Earth is made up of tiny particles called atomos, Greek for "indivisible," from which the modern term "atom" is derived. In the 19th century, John Dalton proposed the atomic theory that is still largely correct today. He put forth five postulates to explain how atoms made up the world around us. (1) All matter is composed of infinitely small particles or atoms. (2) All atoms of a given element are identical to one...
15.5K
Atomic Structure01:33

Atomic Structure

204.5K
Overview
204.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Learning the Action for Long-Time-Step Simulations of Molecular Dynamics.

Physical review letters·2026
Same author

Integrating Charge Equilibration with Equivariant Machine-Learning Interatomic Potentials.

Journal of chemical theory and computation·2026
Same author

Simultaneous learning of static and dynamic charges.

Physical chemistry chemical physics : PCCP·2026
Same author

Bottom-up synthesis of molecular nanodiamond from nanographene.

Nature·2026
Same author

Accelerated Reaction Exploration across Scales: A Hybrid Operando and Modeling Study of Oxidation Kinetics in Monolayer Tungsten Disulfide.

Journal of the American Chemical Society·2026
Same author

How to Train a Shallow Ensemble.

Journal of chemical theory and computation·2026
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Dec 3, 2025

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy
14:55

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

15.8K

Incompleteness of Atomic Structure Representations.

Sergey N Pozdnyakov1, Michael J Willatt1, Albert P Bartók2

  • 1Laboratory of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.

Physical Review Letters
|October 30, 2020
PubMed
Summary
This summary is machine-generated.

Many-body descriptors, crucial for machine learning potentials, are not always overcomplete as believed. This study reveals flaws in three-body correlations, impacting atomic property predictions and offering solutions.

More Related Videos

Interactive Molecular Model Assembly with 3D Printing
06:15

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

10.7K
Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
10:10

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

5.4K

Related Experiment Videos

Last Updated: Dec 3, 2025

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy
14:55

Atomic Scale Structural Studies of Macromolecular Assemblies by Solid-state Nuclear Magnetic Resonance Spectroscopy

Published on: September 17, 2017

15.8K
Interactive Molecular Model Assembly with 3D Printing
06:15

Interactive Molecular Model Assembly with 3D Printing

Published on: August 13, 2020

10.7K
Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures
10:10

Neutron Crystallography Data Collection and Processing for Modelling Hydrogen Atoms in Protein Structures

Published on: December 1, 2020

5.4K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Many-body descriptors are essential for machine-learned interatomic potentials and analyzing atomic structures.
  • A prevailing assumption is that three-body correlations offer an overcomplete description of atomic environments.
  • This assumption has guided the development of models for fitting, classification, and embedding tasks.

Purpose of the Study:

  • To challenge the widespread belief that three-body correlations provide an overcomplete description of atomic environments.
  • To demonstrate the consequences of this potential overcompleteness on machine learning models for atomic structures.
  • To propose solutions for current limitations and anticipate future challenges in accurate atomic property prediction.

Main Methods:

  • The study likely involves theoretical analysis and potentially computational experiments to construct counterexamples.
  • It examines the mathematical properties of many-body descriptors, specifically focusing on three- and four-body correlations.
  • The research analyzes the implications of descriptor properties on machine learning models for atom-centered properties.

Main Results:

  • Several counterexamples are presented, disproving the overcompleteness of three-body correlations in certain contexts.
  • Models using three- or four-body features may yield identical results for distinct atomic configurations, indicating a fundamental deficiency.
  • Writing global properties as sums of atom-centered contributions explains the success of current machine-learning force fields despite this deficiency.

Conclusions:

  • The belief in the overcompleteness of three-body descriptors is not universally true and can lead to inaccuracies.
  • Current machine-learning force fields succeed due to their summation approach, mitigating descriptor deficiencies.
  • Anticipating future accuracy requirements necessitates addressing these fundamental descriptor issues and developing robust solutions.