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相关概念视频

Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

9.5K
The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
9.5K
Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

23.7K
An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
23.7K
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

548
The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.
548
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.0K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.0K
Ladder Diagrams: Complexation Equilibria01:07

Ladder Diagrams: Complexation Equilibria

329
Ladder diagrams are useful for evaluating equilibria involving metal-ligand complexes. The vertical scale of the ladder diagram represents the concentration of unreacted or free ligand, pL. The horizontal lines on the scale depict the log of stepwise formation constants for metal-ligand complexes and indicate the dominant species in all the regions.
The formation constant, K1, for the formation of Cd(NH3)2+ complex from cadmium and ammonia is 3.55 × 102. Log K1 (i.e. pNH3) is 2.55, and...
329
Structural Properties and Dimensions of Lumber01:21

Structural Properties and Dimensions of Lumber

80
Wood's structural properties derive from fibers aligned along the tree's length, contributing significantly to its mechanical strength. Wood exhibits up to twenty times greater tensile strength along these fibers compared to across them, and generally shows better performance under compression than tension. The length of fibers varies, with hardwoods having fibers around one twenty-fifth inch long and softwoods ranging from one-eighth to one-third inch.
The strength characteristics of...
80

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相关实验视频

Updated: Jun 6, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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数据驱动的双向格子属性定制和优化

Fuyuan Liu1,2, Huizhong Wu1,2, Xiaoteng Wu1,2

  • 1School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China.

Materials (Basel, Switzerland)
|November 27, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种基于数据的生成方法,用于晶格材料设计. 它使用机器学习准确预测和优化材料特性,实现显著的性能改进.

关键词:
数据驱动的格子探索.生产性设计是一种创造性设计.格子定制定制 格子定制机器学习是机器学习.参数格子设计的参数格子设计.

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Indirect Fabrication of Lattice Metals with Thin Sections Using Centrifugal Casting
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算工程 计算工程
  • 机器学习应用 机器学习应用

背景情况:

  • 定制和优化格子材料带来了重大设计挑战.
  • 现有的方法往往缺乏满足特定性能要求所需的灵活性和准确性.

研究的目的:

  • 提出一个数据驱动的生成方法来定制和优化格子材料.
  • 开发一种机器学习框架,用于预测弹性模量和优化机械性能.

主要方法:

  • 利用分区建模来对格子结构进行参数描述.
  • 使用同质化方法分析弹性模块并生成数据集.
  • 开发了一个双层机器学习 (ML) 框架,用于前进设计 (弹性模块预测).

主要成果:

  • 与有限元分析相比,双层ML模型在弹性模量预测中实现了不到10%的误差.
  • 在相对密度约束下,优化策略使机械性能在相对密度约束下提高了25%.
  • 通过客户化和优化案例研究证明了实际价值.

结论:

  • 拟议的数据驱动生成方法提供了一种灵活而准确的方法来定制格子材料的特性.
  • 结合生成设计,ML和遗传算法,提高材料性能,有效地导航复杂的设计空间.