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

Ionic Crystal Structures

Ionic Crystal Structures

Ionic crystals consist of two or more different kinds of ions that usually have different sizes. The packing of these ions into a crystal structure is more complex than the packing of metal atoms that are the same size.
Most monatomic ions behave as charged spheres, and their attraction for ions of opposite charge is the same in every direction. Consequently, stable structures for ionic compounds result (1) when ions of one charge are surrounded by as many ions as possible of the opposite...

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Molecular Models

Molecular Models

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.

VSEPR Theory

VSEPR Theory

Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure around a central atom from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them. The electrons in the valence shell of a central atom form either bonding...

VSEPR Theory and the Basic Shapes

VSEPR Theory and the Basic Shapes

Overview of VSEPR Theory

Metallic Solids

Metallic Solids

Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and...

You might also read

Related Articles

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

Sort by

Same author

Central and peripheral neuromuscular mechanisms underlying functional recovery heterogeneity in tibial plateau fractures.

iScience·2026

Same author

Research on the impact of artificial intelligence on the export technological complexity of chinese manufacturing enterprises: An analysis based on mediating effects.

PloS one·2026

Same author

Teacher Emotional Support and Adolescent Student Burnout: A Moderated Mediation Model of Family Cohesion and Meaning in Life.

Behavioral sciences (Basel, Switzerland)·2026

Same author

Variation of active layer thickness and the driving factors in the permafrost region of the Daxing'anling Mountains, Northeast China.

Ying yong sheng tai xue bao = The journal of applied ecology·2026

Same author

Visible-Light-Promoted Tandem Defluorinative Annulation of Trifluoromethyl Benzamides to Access Dibenzo[<i>b</i>,<i>e</i>]azepin-6-ones.

Organic letters·2026

Same author

The photogalvanic effect in 2D van der Waals heterojunctions M<sub>2</sub>XT<sub>2</sub>/SiC <i>via</i> first-principles calculations.

Physical chemistry chemical physics : PCCP·2026

Search research articles

Related Experiment Video

Updated: May 7, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Clustering Cu-S based compounds using periodic table representation and compositional Wasserstein distance.

Shuyan Hao^1,2, Ting Xia², Ruizhi Zhang³

¹Key Laboratory of Computing Power Network and Information Security, Shandong Computer Science Center (National Supercomputing Center in Jinan), Ministry of Education, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250013, Shandong, P. R. China.

Scientific Reports

|December 31, 2024

Summary

This summary is machine-generated.

We developed a new method using Wasserstein distance to measure compositional similarity in materials. This approach helps discover new materials with similar properties by clustering existing compounds, aiding materials data mining.

More Related Videos

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates

Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates

Published on: February 15, 2016

Related Experiment Videos

Last Updated: May 7, 2025

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Spatial Separation of Molecular Conformers and Clusters

Spatial Separation of Molecular Conformers and Clusters

Published on: January 9, 2014

Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates

Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates

Published on: February 15, 2016

Area of Science:

Materials Science
Computational Chemistry
Data Mining

Background:

Analyzing large materials databases is crucial for discovering new materials.
Quantifying compositional and structural similarity is key for materials data mining.
Existing methods may not fully capture nuanced compositional relationships.

Purpose of the Study:

To introduce a novel method for measuring compositional similarity between chemical compounds.
To demonstrate the effectiveness of this method using a dataset of copper-sulfur (Cu-S) compounds.
To facilitate the discovery of new materials with desirable properties through unsupervised clustering.

Main Methods:

Utilized two-dimensional Wasserstein distance (earth mover's distance) to quantify compositional similarity based on periodic table representations.
Integrated compositional similarity with geometrical similarity using local structure order parameters.
Employed the density-based spatial clustering of applications with noise (DBSCAN) algorithm for unsupervised clustering of Cu-S compounds.
Analyzed clustered groups using crystal structure visualization to gain chemical insights.

Main Results:

Successfully clustered 1586 Cu-S compounds from the Inorganic Crystal Structure Database (ICSD) into 86 distinct groups.
Identified a group of rare earth-containing layered Cu-S compounds as potential thermoelectric materials.
Validated the effectiveness of the Wasserstein distance metric and clustering approach through chemical insights from visualized structures.

Conclusions:

The proposed Wasserstein distance method provides an effective way to measure compositional similarity for materials data mining.
Unsupervised clustering based on combined compositional and geometrical similarity aids in understanding materials datasets.
This approach can accelerate the discovery of new materials with similar properties, particularly for applications like thermoelectrics.