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関連する概念動画

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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関連する実験動画

Updated: May 6, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
11:27

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

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MOFClassifier: 計算可能な金属有機フレームワークの検証のための機械学習アプローチ

Guobin Zhao, Pengyu Zhao, Yongchul G Chung

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

    新しい機械学習ツールであるMOFClassifierは,計算可能な金属有機フレームワーク (MOF) を正確に識別します. これは,既存のデータベースとルールベースの方法のエラーを克服することによって,材料の発見を改善します.

    科学分野:

    • 材料科学
    • コンピュータ化学
    • 機械学習

    背景:

    • 高品質の構造データは,金属有機フレームワーク (MOF) の計算上の発見に不可欠です.
    • 現存するMOFのデータベースには重大な誤りがあり,効率的なスクリーニングを妨げています.
    • 現在のルールベースのエラーチェック方法には限界があり,構造を誤って分類します.

    研究 の 目的:

    • 計算可能なMOFの正確な分類のための新しい機械学習アプローチを開発する.
    • MOFデータにおける構造的および化学的エラーを特定する既存の方法の限界を克服する.
    • 新しいMOF材料の大規模計算スクリーニングの信頼性を向上させる.

    主な方法:

    • ポジティブ・ラベルのないクリスタル・グラフ・コンボリューション・ニューラル・ネットワーク (PU-CGCNN) を使用した機械学習モデルであるMOFClassifierを開発した.
    • このモデルは完璧な結晶構造からパターンを学び",結晶類似度スコア" (CLscore) を予測します.
    • ROC値を用いて評価され,既存のルールベースの方法と比較した.

    主要な成果:

    • MOFClassifierはROC値0.979を達成し,以前の最高の0.912を上回りました.

    さらに関連する動画

    Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
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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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    Author Spotlight: Characterizing Porous Materials for Aiding the Development of Robust Metal-Organic Frameworks with Adsorption Behavior

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    Last Updated: May 6, 2026

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    Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
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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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  • このモデルは現在の方法では 見過ごされた 微妙な構造的・化学的エラーを 発見することに成功しました
  • 誤って分類された偽陰性構造を正確に復元し,潜在的なMOF候補を無視するリスクを軽減します.
  • 結論:

    • MOFClassifierは,計算によるスクリーニングのためのMOFを正確に分類する上で重要な進歩を提供します.
    • このツールは新しいMOF材料の発見の効率と信頼性を高めます.
    • 自由に利用でき,CoRE MOF DB 2025 v1.0に統合され,MOFの研究を加速しました.