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Metallic Solids02:37

Metallic Solids

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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....
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Structures of Solids02:22

Structures of Solids

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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...
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Conditions on Early Earth02:06

Conditions on Early Earth

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Around 4 billion years ago, oceans began to condense on earth while volcanic eruptions released nitrogen, carbon dioxide, methane, ammonia, and hydrogen into the primordial atmosphere. However, organisms with the characteristics of life were not initially present on earth. Scientists have used experimentation to determine how organisms evolved that could grow, reproduce, and maintain an internal environment.
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Machines01:19

Machines

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
564
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...
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Molecular and Ionic Solids02:54

Molecular and Ionic Solids

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Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Aprendizaje automático para el descubrimiento basado en datos en geociencia de la Tierra sólida

Karianne J Bergen1,2, Paul A Johnson3, Maarten V de Hoop4

  • 1Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA.

Science (New York, N.Y.)
|March 23, 2019
PubMed
Resumen
Este resumen es generado por máquina.

Las geociencias de la Tierra sólida enfrentan desafíos en la comprensión de procesos subterráneos complejos. El aprendizaje automático ofrece un enfoque prometedor para acelerar el progreso mediante el análisis de más datos y la mejora de las simulaciones por computadora para el descubrimiento de la ciencia de la Tierra.

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Área de la Ciencia:

  • Geociencias de la Tierra sólida
  • La geofísica
  • La geodinámica

Sus antecedentes:

  • Comprender el comportamiento de la Tierra es crucial, pero se ve obstaculizado por procesos complejos y de múltiples escalas.
  • La observación directa del subsuelo de la Tierra es severamente limitada.
  • Los avances en la disponibilidad de datos y las simulaciones computacionales ofrecen nuevas vías para la investigación.

Objetivo del estudio:

  • Revisar el estado actual de las aplicaciones de aprendizaje automático en las geociencias de la Tierra sólida.
  • Identificar los retos y las oportunidades para acelerar los descubrimientos científicos.
  • Proporcionar recomendaciones para ampliar y hacer avanzar el campo.

Principales métodos:

  • Revisión de la literatura existente y estudios de casos sobre el aprendizaje automático en las geociencias.
  • Análisis del potencial de los enfoques basados en datos y las simulaciones avanzadas.
  • Síntesis de las capacidades actuales y las direcciones de investigación futuras.

Principales resultados:

  • El aprendizaje automático (ML) está a punto de desempeñar un papel fundamental en el avance de la ciencia de la Tierra.
  • El aumento de datos y las simulaciones sofisticadas, cuando se combinan con el aprendizaje automático, pueden mejorar la comprensión de los sistemas complejos de la Tierra.
  • La integración del ML requiere abordar desafíos específicos en el manejo de datos y la interpretabilidad del modelo.

Conclusiones:

  • El aprendizaje automático es esencial para superar las complejidades inherentes y las limitaciones de observación en las geociencias de la Tierra sólida.
  • Se recomienda un mayor desarrollo y aplicación de las técnicas ML para acelerar el progreso.
  • La colaboración interdisciplinaria y la inversión estratégica son clave para aprovechar todo el potencial del aprendizaje automático en las ciencias de la Tierra.