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相关概念视频

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

17.6K
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...
17.6K
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.
101.0K
Machines01:19

Machines

564
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

16.1K
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...
16.1K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

20.0K
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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Updated: Jan 27, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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机器学习用于固体地球地质科学的数据驱动发现

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
概括
此摘要是机器生成的。

固体地球地质科学在理解复杂的地下过程方面面临挑战. 机器学习提供了一种有前途的方法,通过分析更多的数据和改进地球科学发现的计算机模拟来加速进步.

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科学领域:

  • 固体地球地质科学
  • 地质学
  • 地动力学

背景情况:

  • 了解地球的行为至关重要,但受到复杂的多层次过程的阻碍.
  • 直接观测地球的地表是非常有限的.
  • 数据可用性和计算模拟的进步为研究提供了新的途径.

研究的目的:

  • 审查固体地球地质科学的机器学习应用的现状.
  • 确定加速科学发现的挑战和机遇.
  • 为扩大和推进该领域提供建议.

主要方法:

  • 对地球科学中的机器学习现有文献和案例研究进行审查.
  • 分析数据驱动方法和先进模拟的潜力.
  • 综合现有能力和未来的研究方向.

主要成果:

  • 机器学习 (ML) 将在推进地球科学方面发挥关键作用.
  • 增加数据和复杂的模拟,当与机器学习相结合时,可以增强对地球复杂系统的理解.
  • 机器学习的整合需要解决数据处理和模型可解释性的特定挑战.

结论:

  • 机器学习对于克服固有的复杂性和固体地球地质科学的观察局限性至关重要.
  • 建议进一步开发和应用ML技术以加快进展.
  • 跨学科合作和战略投资是实现地球科学ML全部潜力的关键.