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Network Covalent Solids02:18

Network Covalent Solids

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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...
14.5K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.9K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.9K
Metallic Solids02:37

Metallic Solids

18.7K
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....
18.7K
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

21.5K
The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...
21.5K
Structural Isomerism02:34

Structural Isomerism

19.7K
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...
19.7K
Properties of Transition Metals02:58

Properties of Transition Metals

27.1K
Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
27.1K

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相关实验视频

Updated: Sep 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

570

在合金中使用图形神经网络和可解释的人工智能进行结构属性链接.

Benjamin Rhoads1, Abigail Hogue1, Lars Kotthoff2

  • 1Department of Mechanical Engineering, University of Mississippi, University, MS 38677, USA.

Materials (Basel, Switzerland)
|August 28, 2025
PubMed
概括

图形神经网络 (GNN) 从微结构中有效预测Ni-Al合金的机械性能,优于卷积神经网络 (CNN). 在材料科学应用中,GNN提供可解释的洞察力,并且需要更少的计算能力.

关键词:
深度学习是一种深度学习.图表神经网络的神经网络机器学习是机器学习.合金合金合金阶段场模拟模拟的相场模拟结构财产联系.

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Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures
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Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures

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Designing Silk-silk Protein Alloy Materials for Biomedical Applications
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Designing Silk-silk Protein Alloy Materials for Biomedical Applications

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算材料科学科学 计算材料科学
  • 材料中的人工智能

背景情况:

  • 深度学习通过预测微观结构与属性关系来加速材料科学.
  • 卷积神经网络 (CNN) 可以分析3D微结构,但需要大量的资源.
  • 在CNN中,对于复杂材料分析的网络规模和培训时间存在限制.

研究的目的:

  • 训练和评估一个图形神经网络 (GNN),用于预测Ni-Al合金的机械性能.
  • 为了比较GNN与CNN的效率和可解释性,用于微观结构分析.
  • 利用可解释的人工智能来更深入地了解物质属性预测.

主要方法:

  • 使用相场建模生成Ni-Al合金微结构.
  • 在这些微结构上训练了一个图形神经网络 (GNN),以预测机械性质演变.
  • 采用突出性分析和贝叶斯推理用于模型解释性和参数确定.

主要成果:

  • 该GNN准确地预测了各种微结构大小和尺寸的合金强化.
  • 与CNN相比,GNN表现出更高的性能,需要更少的GPU利用率.
  • 可解释的人工智能工具为GNN的预测提供了可解释的见解.

结论:

  • GNN提供了一种准确,高效和可解释的方法,用于从材料微观结构中提取信息.
  • 在微观结构大小和维度限制方面,GNN克服了CNN的局限性.
  • 这种方法推进了材料性质的预测,并通过可解释的AI增强了理解.