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相关概念视频

Classification of Elements and Compounds02:54

Classification of Elements and Compounds

68.4K
Pure substances consist of only one type of matter. A pure substance can be an element or a compound. An element consists of only one type of atom, while a compound consists of two or more types of atoms held together by a chemical bond. Elements are classified as atomic or molecular based on the nature of their basic units.
Compounds are pure substances composed of two or more elements in fixed, definite proportions. Compounds are classified as ionic or molecular (covalent) based on the bonds...
68.4K
Metallic Solids02:37

Metallic Solids

16.5K
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...
16.5K
Metal-Ligand Bonds02:51

Metal-Ligand Bonds

19.3K
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...
19.3K
Properties of Organometallic Compounds01:23

Properties of Organometallic Compounds

2.1K
Organometallic compounds are compounds that contain a carbon–metal bond. Carbon belongs to an organyl group like alkyl, aryl, allyl, or benzyl groups. The metal can be from Group I or Group II of the periodic table, a transition metal, or a semimetal.
2.1K
Structural Classification of Joints01:20

Structural Classification of Joints

8.1K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
8.1K
Classification and Mechanical Properties of Synthetic Polymers01:28

Classification and Mechanical Properties of Synthetic Polymers

22
Synthetic polymers are classified as elastomers, fibers, or plastics based on their crystallinity. Crystallinity, the degree of long-range order in the solid state, influences the mechanical properties (stretching or contracting) of elastomers. Elastomers are flexible polymers that can expand or contract easily upon the application of an external force. They have numerous crosslinks that pull them back into their original shape when stress is removed. Silicones, for instance, are highly elastic...
22

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相关实验视频

Updated: May 6, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
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Synthesis and Characterization of Functionalized Metal-organic Frameworks

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MOFClassifier:用于验证计算准备的金属有机框架的机器学习方法

Guobin Zhao, Pengyu Zhao, Yongchul G Chung

    Journal of the American Chemical Society
    |August 11, 2025
    PubMed
    概括

    一个新的机器学习工具,MOFClassifier,准确地识别了可计算的金属有机框架 (MOF). 这可以通过克服现有数据库和基于规则的方法中的错误来改善材料的发现.

    科学领域:

    • 材料科学
    • 计算化学
    • 机器学习

    背景情况:

    • 高质量的结构数据对于金属有机框架 (MOF) 的计算发现至关重要.
    • 现有的MOF数据库存在重大错误,阻碍了有效的选.
    • 目前基于规则的错误检查方法有局限性,并错误分类结构.

    研究的目的:

    • 开发一种新的机器学习方法,用于准备计算的MOF的准确分类.
    • 克服在MOF数据中识别结构和化学错误的现有方法的局限性.
    • 提高新MOF材料大规模计算选的可靠性.

    主要方法:

    • 开发了一种机器学习模型MOFClassifier,该模型使用正无标记晶体图卷积神经网络 (PU-CGCNN).
    • 该模型从完美的晶体结构中学习模式,以预测"晶体相似度得分" (CLscore).
    • 使用ROC值评估性能,并与现有的基于规则的方法进行比较.

    主要成果:

    • MOFClassifier的ROC值为0.979,超过了之前的0.912.
    • 该模型成功地发现了当前方法所遗漏的微妙结构和化学错误.
    • 准确地恢复错误分类的错误负面结构,减少忽视潜在的MOF候选者的风险.

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    Author Spotlight: Characterizing Porous Materials for Aiding the Development of Robust Metal-Organic Frameworks with Adsorption Behavior
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    相关实验视频

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    Author Spotlight: Characterizing Porous Materials for Aiding the Development of Robust Metal-Organic Frameworks with Adsorption Behavior

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    结论:

    • 在计算选中,MOFClassifier在准确分类MOF方面取得了重大进展.
    • 该工具提高了发现新MOF材料的效率和可靠性.
    • 免费提供并集成到CoRE MOF DB 2025 v1.0中,加速MOF研究.