Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Molecular Orbital Theory I

Molecular Orbital Theory I

Overview of Molecular Orbital Theory

Atomic Orbitals

Atomic Orbitals

An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Hybridization of Atomic Orbitals I

Hybridization of Atomic Orbitals I

The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...

Electron Orbital Model

Electron Orbital Model

Orbitals are the areas outside of the atomic nucleus where electrons are most likely to reside. They are characterized by different energy levels, shapes, and three-dimensional orientations. The location of electrons is described most generally by a shell or principal energy level, then by a subshell within each shell, and finally, by individual orbitals found within the subshells.
The first shell is closest to the nucleus, and it has only one subshell with a single spherical orbital called the...

MO Theory and Covalent Bonding

MO Theory and Covalent Bonding

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...

You might also read

Related Articles

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

Sort by

Same author

Machine-Learning-Guided Discovery of CH<sub>4</sub> Combustion Catalysts Operating in the Presence of SO<sub>2</sub>.

Journal of the American Chemical Society·2026

Same author

The role of the tryptophan-rich allosteric network and sodium egress in GPCR activation.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.

Chemical reviews·2026

Same author

DiffBreed: automatic differentiation enables efficient gradient-based optimization of breeding strategies.

Bioinformatics (Oxford, England)·2025

Same author

Harnessing Explainable AI to Explore Structure-Activity Relationships in Artificial Olfaction.

ACS applied materials & interfaces·2025

Same author

GPepT: A Foundation Language Model for Peptidomimetics Incorporating Noncanonical Amino Acids.

ACS medicinal chemistry letters·2025

Same journal

Current status of room temperature magnetic compensation in impurity-doped Mn<sub>4</sub>N epitaxial thin films.

Science and technology of advanced materials·2026

Same journal

Group 8 metallocenes as single-source precursors for the synthesis of light-element-stabilized FCC phases under extreme conditions.

Science and technology of advanced materials·2026

Same journal

Reproducible chiroptical activity from aggregated chiral thienopyrroledione-fluorene π‑conjugated polymers.

Science and technology of advanced materials·2026

Same journal

Wet etching of (-102) β-Ga<sub>2</sub>O<sub>3</sub> with tetramethylammonium hydroxide (TMAH).

Science and technology of advanced materials·2026

Same journal

A novel approach to micro-fabricated thermoelectric generators with SrTiO<sub>3</sub>.

Science and technology of advanced materials·2026

Same journal

Probing the Hall anomaly and electronic structure in kagome metal RbV<sub>3</sub>Sb<sub>5</sub> under hydrostatic pressure.

Science and technology of advanced materials·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 18, 2026

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

Machine learning reveals orbital interaction in materials.

Tien Lam Pham^1,2, Hiori Kino^2,3, Kiyoyuki Terakura^1,3

¹Japan Advanced Institute of Science and Technology, Nomi, Japan.

Science and Technology of Advanced Materials

|November 21, 2017

Summary

This summary is machine-generated.

We introduce the orbital-field matrix (OFM), a new material representation based on electron distribution. This method accurately predicts material properties and reveals insights into magnetic moments, aiding materials data mining.

Keywords:

Material descriptor data mining machine learning magnetic materials material informatics

More Related Videos

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

Revealing Dynamic Processes of Materials in Liquids Using Liquid Cell Transmission Electron Microscopy

Revealing Dynamic Processes of Materials in Liquids Using Liquid Cell Transmission Electron Microscopy

Published on: December 20, 2012

Related Experiment Videos

Last Updated: Feb 18, 2026

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

Revealing Dynamic Processes of Materials in Liquids Using Liquid Cell Transmission Electron Microscopy

Revealing Dynamic Processes of Materials in Liquids Using Liquid Cell Transmission Electron Microscopy

Published on: December 20, 2012

Area of Science:

Materials Science
Computational Chemistry
Condensed Matter Physics

Background:

Accurate prediction of material properties is crucial for discovering new materials.
Existing methods for material data mining can be computationally intensive.
Understanding electron distribution is key to predicting material behavior.

Purpose of the Study:

To introduce a novel material representation called the orbital-field matrix (OFM).
To demonstrate the utility of OFM in material data mining.
To accurately predict material properties like formation energies and magnetic moments.

Main Methods:

Developing the orbital-field matrix (OFM) based on valence shell electron distribution.
Applying OFM to predict formation energies of crystalline materials.
Using OFM to predict atomization energies of molecular materials.
Employing OFM for predicting local magnetic moments in bimetal alloys.

Main Results:

OFM enables highly accurate predictions of formation energies and atomization energies.
OFM accurately predicts local magnetic moments in lanthanide and transition-metal alloys.
Decision tree regression on OFM reveals the role of coordination numbers in determining magnetic moments.

Conclusions:

The orbital-field matrix (OFM) is a powerful tool for material data mining.
OFM facilitates accurate prediction of diverse material properties.
OFM provides direct insights into structure-property relationships, such as the influence of coordination on magnetism.