Jove
Visualize
Contact Us

Related Concept Videos

Ionic Crystal Structures02:42

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...
Network Covalent Solids02:18

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
Neural Circuits01:25

Neural Circuits

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...
Crystal Density01:19

Crystal Density

The crystal lattice structure of a material allows us to determine how many molecules exist in its unit cell. With this information, alongside the unit-cell parameters - three distance parameters (a, b, c) and three angular parameters (α, β, γ).Density (ρ) = (Z × M) / (a × b × c × NA)where:Z is the number of formula units per unit cellM is the molar mass of the substancea, b, and c are the edge lengths of the unit cellNA is Avogadro’s numberFor a simple cubic lattice, atoms are located only at...
Lattice Energies of Ionic Crystals01:27

Lattice Energies of Ionic Crystals

Lattice energy represents the energy released when gaseous cations and anions combine to form an ionic solid, reflecting the strength of electrostatic interactions within the crystal. This process is fundamentally governed by Coulombic attraction between oppositely charged ions, where the potential energy varies inversely with the interionic distance and directly with the product of ionic charges. As ions approach one another, the electrostatic energy becomes increasingly negative, indicating a...
Imperfections in Crystal Structure: Non-Stoichiometric Defects01:29

Imperfections in Crystal Structure: Non-Stoichiometric Defects

Non-stoichiometric defects refer to a type of defect in the crystal structure of a compound where the ratio of its constituent elements deviates from the ideal stoichiometric ratio. There are two main types of non-stoichiometric defects: metal excess defects and metal deficiency defects.Metal excess defects occur when there is a slight surplus of metal ions than what is required by the stoichiometric ratio of the compound. For example, heating a sodium chloride crystal in sodium vapor results...

You might also read

Related Articles

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

Sort by
Same author

CoRE 2D-HOIP DB: Computation-Ready, Experimental Database of Two-Dimensional Hybrid Organic-Inorganic Perovskites.

Journal of chemical information and modeling·2026
Same author

Symmetry-Sensitive Analysis of Molecular Graph Neural Network Models.

Journal of chemical information and modeling·2026
Same author

A Fast and Accurate Semi-Empirical Approach for Hydrogen-Exchange Kinetic Isotope Effect Evaluation.

Journal of computational chemistry·2026
Same author

The role of neptunium oxidation states and coordination in shaping XANES spectra at the Np L<sub>3</sub> absorption edge.

Physical chemistry chemical physics : PCCP·2026
Same author

Machine Learning Photodynamics Unveils a Controlled H<sub>2</sub> Loss Channel in the Methaniminium Cation.

The journal of physical chemistry letters·2025
Same author

gSelformer-MV: Multiview, Subgraph-Augmented Group SELFIES Transformer for Molecular Property Prediction.

Journal of chemical information and modeling·2025
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 Experiment Video

Updated: Jun 23, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

8.5K

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design.

Vadim Korolev1,2, Artem Mitrofanov1,2

  • 1Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia.

Journal of Chemical Information and Modeling
|March 8, 2024
PubMed
Summary

This study introduces a coarse-grained crystal graph neural network for reticular materials, offering accurate property prediction with lower computational costs. This approach challenges atom-centric methods in materials design.

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.7K
Author Spotlight: Enhancing CryoEM Resolution Using Graphene-Coated Grids
06:53

Author Spotlight: Enhancing CryoEM Resolution Using Graphene-Coated Grids

Published on: September 8, 2023

3.1K

Related Experiment Videos

Last Updated: Jun 23, 2026

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

8.5K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

1.7K
Author Spotlight: Enhancing CryoEM Resolution Using Graphene-Coated Grids
06:53

Author Spotlight: Enhancing CryoEM Resolution Using Graphene-Coated Grids

Published on: September 8, 2023

3.1K

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Reticular materials like metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have diverse applications.
  • Predicting properties of these materials is complex due to vast chemical space.
  • Current AI models use atomic-level graphs, which can be computationally expensive and include redundant features.

Purpose of the Study:

  • To develop a more efficient materials representation for property prediction.
  • To overcome limitations of atomic-level graph neural networks in reticular materials design.
  • To introduce a coarse-grained crystal graph approach for inverse materials design.

Main Methods:

  • Developed a coarse-grained crystal graph representation focusing on molecular building units.
  • Assessed neural network performance using composition-based, crystal-structure-aware, and coarse-grained models.
  • Evaluated predictive accuracy and energy efficiency of different representations.

Main Results:

  • Coarse-grained crystal graph neural networks demonstrated competitive accuracy with significantly lower computational costs.
  • The proposed method is a viable alternative to atomic-level graph neural networks.
  • Models were successfully integrated into an inverse materials design pipeline.

Conclusions:

  • The coarse-grained crystal graph framework offers an efficient and accurate approach for reticular materials property prediction.
  • This method challenges the traditional atom-centric perspective in materials design.
  • It provides a valuable tool for accelerating the discovery of novel reticular materials.