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Related Concept Videos

Metallic Solids02:37

Metallic Solids

20.5K
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 malleability....
20.5K
Network Covalent Solids02:18

Network Covalent Solids

16.0K
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...
16.0K
Structures of Solids02:22

Structures of Solids

17.5K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
17.5K
Structural Isomerism02:34

Structural Isomerism

21.5K
Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula. Structural isomerism of coordination compounds can be divided into two subcategories, the linkage isomers and coordination-sphere isomers.
Linkage isomers occur when the coordination compound contains a ligand that can bind to the transition metal center through two different atoms. For example, the CN− ligand can bind through the carbon atom or through the nitrogen atom. Similarly, SCN− can...
21.5K
Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

11.4K
The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
11.4K
Valence Bond Theory02:42

Valence Bond Theory

11.2K
Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
11.2K

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Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
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Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

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Explainable GNN-derived structure-property relationships in interstitial-alloy materials.

Eduardo Aguilar-Bejarano1,2,3, Luis Arrieta4, Mauricio Gutiérrez5

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Graph neural networks (GNNs) accurately predict material properties, outperforming traditional models. A new tool, CGExplainer, reveals atomic arrangements crucial for material design, accelerating discovery.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Data Science

Background:

  • Understanding structure-property relationships is crucial for designing novel materials.
  • Traditional methods for predicting material properties can be data-intensive and lack interpretability.
  • Non-stoichiometric materials and interstitial alloys present unique challenges due to their variable compositions.

Purpose of the Study:

  • To develop and apply graph neural networks (GNNs) for predicting properties of non-stoichiometric materials.
  • To introduce an interpretable GNN framework for analyzing structure-property relationships.
  • To demonstrate the superiority of GNNs over traditional interatomic potential models (IAPs) in accuracy and data efficiency.

Main Methods:

  • Application of the crystal graph convolutional network (CGCNet) to predict properties of Mo2C and Ti2C.
  • Development of the crystal graph explainer (CGExplainer) for model interpretability.
  • Comparison of GNN performance against traditional human-derived interatomic potential models (IAPs).

Main Results:

  • CGCNet demonstrated higher prediction accuracy and data efficiency compared to IAPs.
  • Significant improvements were observed in the ability of GNNs to extrapolate properties to larger supercells.
  • CGExplainer successfully identified key atomic arrangements governing material properties.

Conclusions:

  • GNN-based approaches offer a powerful and efficient framework for materials discovery.
  • The developed methodology accelerates the design of materials with tailored properties, especially for alloys with variable compositions.
  • This work extends the applicability of GNNs to a wider range of complex material systems.