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Related Concept Videos

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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Crystal Field Theory - Octahedral Complexes02:58

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Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
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Surface Segregation Studies in Ternary Noble Metal Alloys: Comparing DFT and Machine Learning with Experimental Data.

Kirby Broderick1, Robert A Burnley1, Andrew J Gellman1

  • 1Carnegie Mellon University Department of Chemical Engineering, 5000 Forbes Ave, Pittsburgh, Pennsylvania, 15213, United States.

Chemphyschem : a European Journal of Chemical Physics and Physical Chemistry
|March 22, 2024
PubMed
Summary

This study models surface segregation in noble metal alloys using machine learning and simulations. The (110) crystal orientation best predicts experimental trends, though simulation accuracy needs improvement.

Keywords:
Monte Carlodensity functional calculationsmachine learningsurface chemistrysurface segregation

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Surface Science

Background:

  • Surface segregation, where alloy surface composition differs from bulk, is challenging to study due to the vast composition space.
  • Noble and platinum-group metal alloys are catalytically relevant, making their surface segregation crucial for applications.

Purpose of the Study:

  • To investigate surface segregation in ternary AgAuCu, AuCuPd, and CuPdPt alloys.
  • To develop and validate a computational approach combining Density Functional Theory (DFT) and machine learning for predicting surface segregation.
  • To compare simulation results with experimental data for AgAuCu and AuCuPd systems.

Main Methods:

  • Generated a dataset of 2478 face-centered cubic (fcc) alloy slabs across three low-index crystallographic orientations.
  • Relaxed structures using DFT with the PBEsol functional and D3 dispersion corrections.
  • Trained a machine learning model on DFT data and employed it in 1800 Monte Carlo simulations for each ternary system and orientation.

Main Results:

  • The (110) crystallographic orientation demonstrated the closest agreement with experimentally observed surface segregation trends.
  • Simulations qualitatively matched experimental observations, indicating the predictive capability of the developed model.
  • Identified limitations in the PBEsol functional's accuracy for quantitative predictions.

Conclusions:

  • The study advances the understanding of surface segregation in catalytically important alloys.
  • It highlights the utility of integrated DFT, machine learning, and Monte Carlo simulations for materials research.
  • Emphasizes the need for enhanced accuracy in computational methods for reliable materials design.