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Valence Bond Theory02:45

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Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
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Compared to ionic bonds, which results from the transfer of electrons between metallic and nonmetallic atoms, covalent bonds result from the mutual attraction of atoms for a “shared” pair of electrons.
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Polymorphism refers to the existence of a drug substance in multiple crystalline forms, known as polymorphs. Recently, this term has been expanded to include solvates (forms containing a solvent), amorphous forms (non-crystalline forms), and desolvated solvates (forms from which the solvent has been removed).
Some polymorphic crystals possess lower aqueous solubility than their amorphous counterparts, leading to incomplete absorption. For instance, the oral suspension of Chloramphenicol, which...
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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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基于机器学习的XANES分析用于预测无形亚氧化物中的局部结构和价值.

Yu Fujikata1,2, Hiroki Sugisawa2, Teruyasu Mizoguchi1

  • 1Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan. fujikata@iis.u-tokyo.ac.jp.

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这项研究引入了一种机器学习模型,该模型可以预测子氧化.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 机器学习 机器学习

背景情况:

  • 子氧化 (SiOx) 根据其成分表现出可调节的特性,使其在工业上具有重要意义.
  • 准确地将SiOx的原子结构与其属性的相关性与传统方法具有挑战性.
  • 了解SiO需要对其电子价值和局部原子结构进行定量分析.

研究的目的:

  • 开发一种机器学习模型,用于从X射线吸收近边缘结构 (XANES) 光谱中预测原子价值状态和Si-O辐射分布函数.
  • 建立一个强大的和实验上可行的框架来表征无形子材料.

主要方法:

  • 使用分子动力学模拟生成了九个无形SiOx网络.
  • 使用第一原则计算计算了Si K边缘XANES光谱.
  • 在生成的XANES光谱数据集上训练了一个深度神经网络.

主要成果:

  • 深度神经网络模型准确地预测了来自XANES光谱的局部价值状态和Si-O辐射分布函数.
  • 特定的光谱区域被确定为对价值状态 (边缘附近) 和结构 (更高能量) 预测至关重要.
  • 该模型表现出强性,在组合平均光谱上保持高性能.

结论:

  • 开发的机器学习方法可以从XANES无形材料的光谱中直接提取电子和结构信息.
  • 这种方法克服了分析复杂,多价值无形系统的重大瓶.
  • 该框架通过定量表征组成-结构-属性关系,促进了基于SiOx的功能材料的加速开发.