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相关概念视频

Hydrogen Bonds01:04

Hydrogen Bonds

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A hydrogen bond is formed when a weakly positive hydrogen atom already bonded to one electronegative atom (for example, the oxygen in the water molecule) is attracted to another electronegative atom from another polar molecule, such as water (H2O), hydrogen fluoride (HF), or ammonia (NH3). The huge electronegativity difference between the H atom (2.1) and the atom to which it is bonded (4.0 for an F atom, 3.5 for an O atom, or 3.0 for an N atom), combined with the very small size of an H atom...
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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...
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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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Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
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VSEPR Theory for Determination of Electron Pair Geometries
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Author Spotlight: Accelerating Discovery in Microporous Material Chemistry
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在金属有机框架中探索储能能力:贝叶斯式优化方法

Sumedh Ghude1, Chandra Chowdhury2

  • 1Department of Physics, Indian Institute of Technology Madras, Chennai, 600036, India.

Chemistry (Weinheim an der Bergstrasse, Germany)
|August 28, 2023
PubMed
概括
此摘要是机器生成的。

贝叶斯优化 (BO) 有效地选用于 (H2) 储存的金属有机框架 (MOF),以最小的计算确定顶级候选者. 这种人工智能方法显著减少了材料发现的实验力度.

关键词:
MOFs 的使用情况.贝叶斯的优化是贝叶斯的优化.进化性搜索 进化性搜索储存的储存的储存.粒子群集优化 粒子群集优化

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 人工智能的人工智能

背景情况:

  • 金属有机框架 (MOF) 对于气体储存,捕获和传感至关重要.
  • 大型MOF数据库的高吞吐量选,以获得最佳吸附性能,在计算上是昂贵的.
  • 需要有效的方法来确定气体分离和储存应用的有希望的MOF.

研究的目的:

  • 证明贝叶斯优化 (BO) 对于估计MOF中的H2吸收的有效性.
  • 显著降低选大型MOF数据库的计算成本.
  • 为优化MOF属性和材料设计见解提供一个框架.

主要方法:

  • 利用现有的98,000个真实和假设的MOF数据集.
  • 应用贝叶斯优化 (BO) 来预测H2吸收能力.
  • 将BO与粒子群优化 (PSO) 和一种新的进化PSO (EPSO) 变体进行比较.

主要成果:

  • 通过对数据库中不到0.027%的数据进行选,BO确定了顶级候选MOF.
  • 该BO方法显著降低了MOF查所需的实验力度.
  • 公共服务局和EPSO在估计H2吸收潜力方面取得了与BO相似的结果.

结论:

  • 贝叶斯优化是一种高效的工具,可以加速发现用于H2存储的MOF.
  • 开发的框架为优化各种材料性能提供了可转移的方法.
  • 像BO和PSO这样的人工智能驱动的方法为材料科学提供了强大的替代品,而不是传统的计算选.