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Classification of Elements and Compounds02:54

Classification of Elements and Compounds

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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...
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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

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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

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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

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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...
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Synthesis and Characterization of Functionalized Metal-organic Frameworks
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MOFClassifier: Un enfoque de aprendizaje automático para la validación de marcos metálicos orgánicos listos para el

Guobin Zhao, Pengyu Zhao, Yongchul G Chung

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    |August 11, 2025
    PubMed
    Resumen

    Una nueva herramienta de aprendizaje automático, MOFClassifier, identifica con precisión los marcos metálico-orgánicos (MOF) listos para el cálculo. Esto mejora el descubrimiento de materiales al superar los errores en las bases de datos existentes y los métodos basados en reglas.

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    Área de la Ciencia:

    • Ciencias de los materiales
    • Química computacional
    • Aprendizaje automático

    Sus antecedentes:

    • Los datos estructurales de alta calidad son cruciales para el descubrimiento computacional de marcos metálico-orgánicos (MOF).
    • Las bases de datos existentes del Ministerio de Fomento contienen errores significativos que obstaculizan el control eficiente.
    • Los métodos actuales de verificación de errores basados en reglas tienen limitaciones y clasifican erróneamente las estructuras.

    Objetivo del estudio:

    • Desarrollar un nuevo enfoque de aprendizaje automático para la clasificación precisa de los MOF listos para el cálculo.
    • Superar las limitaciones de los métodos existentes en la identificación de errores estructurales y químicos en los datos MOF.
    • Mejorar la fiabilidad del cribado computacional a gran escala para nuevos materiales MOF.

    Principales métodos:

    • Desarrolló MOFClassifier, un modelo de aprendizaje automático que utiliza una red neuronal convolucional de gráficos de cristal sin etiqueta positiva (PU-CGCNN).
    • El modelo aprende patrones de estructuras cristalinas perfectas para predecir una "puntuación de semejanza de cristal" (CLscore).
    • Evaluación del rendimiento utilizando valores de ROC y comparación con los métodos basados en reglas existentes.

    Principales resultados:

    • MOFClassifier logró un valor de ROC de 0,979, superando el mejor anterior de 0,912.
    • El modelo identificó con éxito los errores estructurales y químicos sutiles que los métodos actuales omiten.
    • Recuperación precisa de estructuras falsas negativas clasificadas erróneamente, reduciendo el riesgo de pasar por alto posibles candidatos a MOF.

    Conclusiones:

    • MOFClassifier ofrece un avance significativo en la clasificación precisa de las MOF para la detección computacional.
    • La herramienta mejora la eficiencia y la fiabilidad del descubrimiento de nuevos materiales MOF.
    • Disponible de forma gratuita e integrado en el CoRE MOF DB 2025 v1.0, acelerando la investigación en MOF.