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Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

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...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Metallic Solids02:37

Metallic Solids

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. Many...
Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

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:
Crystal Field Theory - Tetrahedral and Square Planar Complexes02:46

Crystal Field Theory - Tetrahedral and Square Planar Complexes

Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than the dxy,...

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Related Experiment Video

Updated: Jul 10, 2026

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

Embedded cluster density approximation for scalable high-level exchange-correlation calculations in periodic systems.

Mani Tyagi1, Chen Huang2

  • 1Department of Scientific Computing, Florida State University, Tallahassee, Florida 32306, USA.

The Journal of Chemical Physics
|July 9, 2026
PubMed
Summary
This summary is machine-generated.

The embedded cluster density approximation (ECDA) method scales up high-level electronic-structure calculations for periodic systems. This approach accurately predicts electronic properties in large systems using localized, high-level computations.

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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
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Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

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Last Updated: Jul 10, 2026

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Area of Science:

  • Computational chemistry
  • Materials science
  • Quantum mechanics

Background:

  • High-level electronic-structure methods are crucial for accurate property prediction but computationally expensive for large systems.
  • Scaling these methods to larger systems is essential for broader applicability in materials science and chemistry.

Purpose of the Study:

  • Extend the embedded cluster density approximation (ECDA) method to periodic systems.
  • Enable accurate and efficient calculation of electronic properties for large, periodic materials.

Main Methods:

  • Adapted ECDA, originally for finite systems, to handle periodic boundary conditions.
  • Utilized density functional embedding theory to seamlessly embed cluster densities within the larger system.
  • Calculated high-level exchange-correlation (XC) energies on local clusters and projected them onto central atoms for system-wide energy construction.

Main Results:

  • Demonstrated ECDA's applicability as a nearly black-box method across various bond types in periodic systems.
  • Achieved good accuracy with modest cluster sizes, showing efficiency gains.
  • Successfully calculated energy differences and generated smooth energy surfaces, highlighting the method's robustness.

Conclusions:

  • ECDA provides an efficient and accurate approach for electronic property prediction in large periodic systems.
  • The density partitioning in ECDA facilitates local active space definition, leading to appealing computational features.
  • Potential convergence challenges in covalent systems due to boundary effects require further investigation.