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Related Concept Videos

Phase Diagrams02:39

Phase Diagrams

45.4K
A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...
45.4K
Properties of Transition Metals02:58

Properties of Transition Metals

27.8K
Transition metals are defined as those elements that have partially filled d orbitals. As shown in Figure 1, the d-block elements in groups 3–12 are transition elements. The f-block elements, also called inner transition metals (the lanthanides and actinides), also meet this criterion because the d orbital is partially occupied before the f orbitals.
27.8K
Phase Diagram01:19

Phase Diagram

6.3K
The phase of a given substance depends on the pressure and temperature. Thus, plots of pressure versus temperature showing the phase in each region provide considerable insights into the thermal properties of substances. Such plots are known as phase diagrams. For instance, in the phase diagram for water (Figure 1), the solid curve boundaries between the phases indicate phase transitions (i.e., temperatures and pressures at which the phases coexist).
6.3K
Properties of Organometallic Compounds01:23

Properties of Organometallic Compounds

1.2K
Organometallic compounds are compounds that contain a carbon–metal bond. Carbon belongs to an organyl group like alkyl, aryl, allyl, or benzyl groups. The metal can be from Group I or Group II of the periodic table, a transition metal, or a semimetal.
1.2K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

28.5K
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...
28.5K
MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

12.5K
The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
12.5K

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Updated: Oct 20, 2025

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
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Phase-Property Diagrams for Multicomponent Oxide Systems toward Materials Libraries.

Leonardo Velasco1, Juan S Castillo1,2,3, Mohana V Kante1,3

  • 1Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.

Advanced Materials (Deerfield Beach, Fla.)
|September 13, 2021
PubMed
Summary
This summary is machine-generated.

High-throughput methods accelerate the discovery of novel high entropy materials. Machine learning and graphical diagrams aid in understanding complex compositional relationships and properties.

Keywords:
high entropy materialshigh-throughput techniquesmachine learningmaterials librariesphase diagramvirtual materials

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Area of Science:

  • Materials Science
  • Solid State Chemistry
  • Computational Materials Science

Background:

  • Discovering new multicomponent materials is time-consuming and expensive using traditional methods.
  • High entropy materials offer vast compositional possibilities but are challenging to explore.
  • Automated and computational approaches are needed to accelerate materials discovery.

Purpose of the Study:

  • To develop and apply high-throughput experimental techniques for fabricating and characterizing high entropy oxides.
  • To create intuitive visualization tools correlating material properties with composition and structure.
  • To implement interpretable machine learning models for efficient data analysis.

Main Methods:

  • Automated high-throughput synthesis and characterization of high entropy oxides.
  • Development of a graphical phase-property diagram for intuitive data visualization.
  • Training interpretable machine learning models for automated data analysis and property prediction.

Main Results:

  • Successful fabrication and characterization of high entropy oxides using automated techniques.
  • Introduction of a phase-property diagram for visualizing structure, composition, and band gap relationships.
  • Demonstration of machine learning models for accelerated data comprehension and analysis.

Conclusions:

  • High-throughput methods combined with machine learning significantly expedite the exploration of multicomponent material systems.
  • The developed graphical and machine learning tools facilitate the understanding and virtual development of novel functional and structural materials.
  • This approach opens new avenues for rapid materials discovery and design.