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Metallic Solids

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....

Cluster Sampling Method

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.
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For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
For instance, group IV...

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Updated: Sep 17, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

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Published on: September 8, 2023

Exploring Quantum Computing for Metal Cluster Analysis.

Nia Pollard¹, A'Laura C Hines¹, Andre Z Clayborne^1,2

¹Department of Chemistry and Biochemistry, George Mason University, Fairfax, Virginia 22030, United States.

The Journal of Physical Chemistry. A

|June 27, 2025

Summary

This summary is machine-generated.

Quantum computing enhances metal cluster analysis by integrating quantum-DFT embedding. This workflow improves electronic structure modeling for materials discovery, despite current hardware limitations.

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Area of Science:

Computational Chemistry
Quantum Computing
Materials Science

Background:

Classical computational methods face limitations in chemical accuracy and efficiency for nanoscale systems.
Developing advanced computational workflows is crucial for accurate materials modeling.
Quantum computing offers a potential avenue for overcoming classical limitations.

Purpose of the Study:

To develop and implement a quantum-DFT embedding workflow for metal cluster analysis.
To leverage quantum computing for improved electronic structure modeling.
To assess the capabilities and limitations of near-term quantum devices in computational chemistry.

Main Methods:

Integration of the Variational Quantum Eigensolver (VQE) with Density Functional Theory (DFT).
Application of the quantum-DFT embedding workflow to aluminum and gold clusters.
Testing the workflow's ability to determine electronic properties and catalytic potential.

Main Results:

Successfully determined electronic properties for aluminum clusters up to Al7-.
Investigated gold clusters for nitric oxide reduction potential.
Identified challenges including memory limitations, lack of relativistic corrections, and open-shell system handling.

Conclusions:

Quantum DFT embedding shows potential for advancing materials discovery and nanomaterial design.
Current quantum hardware and algorithms require further development for complex chemical systems.
This proof-of-concept study highlights the promise of quantum computing in computational chemistry.