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Related Concept Videos

Ionic Crystal Structures02:42

Ionic Crystal Structures

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Ionic crystals consist of two or more different kinds of ions that usually have different sizes. The packing of these ions into a crystal structure is more complex than the packing of metal atoms that are the same size.
Most monatomic ions behave as charged spheres, and their attraction for ions of opposite charge is the same in every direction. Consequently, stable structures for ionic compounds result (1) when ions of one charge are surrounded by as many ions as possible of the opposite...
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Ions are atoms or molecules bearing an electrical charge. A cation (a positive ion) forms when a neutral atom loses one or more electrons from its valence shell, and an anion (a negative ion) forms when a neutral atom gains one or more electrons in its valence shell. Compounds composed of ions are called ionic compounds (or salts), and their constituent ions are held together by ionic bonds: electrostatic forces of attraction between oppositely charged cations and anions. 
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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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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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Isomerism in Complexes
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Combining Solid-state and Solution-based Techniques: Synthesis and Reactivity of ChalcogenidoplumbatesII or IV
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Tackling Structural Complexity in Li2S-P2S5 Solid-State Electrolytes Using Machine Learning Potentials.

Carsten G Staacke1, Tabea Huss1, Johannes T Margraf1

  • 1Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany.

Nanomaterials (Basel, Switzerland)
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

We developed a machine-learning potential for lithium thiophosphate solid-state electrolytes, enabling efficient simulation of ion conductivity. This approach accurately predicts material properties and reveals the impact of anion dynamics on performance.

Keywords:
Li-ion batteryamorphoushigh ionic conductivity solid electrolytemachine learning

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Electrochemistry

Background:

  • Lithium thiophosphate (LPS) materials are promising solid-state electrolytes (SSEs) for lithium-ion batteries, offering high ionic conductivity and cost-effectiveness.
  • The performance of LPS materials is significantly influenced by their complex microchemistry and structural disorder.
  • Traditional ab initio calculations face limitations in simulating industrially relevant LPS materials due to time and length scale constraints.

Purpose of the Study:

  • To develop a versatile machine-learning interatomic potential for the lithium thiophosphate (LPS) material class.
  • To overcome computational limitations for simulating LPS materials at industrially relevant scales.
  • To investigate the influence of thiophosphate subunits on lithium ion conductivity in LPS.

Main Methods:

  • Development and training of a Gaussian Approximation Potential (GAP) for LPS materials, capable of describing both crystalline and glassy states.
  • Application of the GAP surrogate model to simulate lithium ion conductivity in various LPS compositions (Li3PS4, Li7P3S11, and xLi2S-(100-x)P2S5 glasses).
  • Analysis of thiophosphate subunit structures and anion dynamics to understand their effect on ionic transport.

Main Results:

  • The trained GAP accurately reproduces material properties, aligning well with experimental findings.
  • Simulations revealed the critical role of anion dynamics in determining lithium ion conductivity, particularly in glassy LPS.
  • The machine-learning potential demonstrates transferability to other solid-state electrolyte materials.

Conclusions:

  • The developed machine-learning interatomic potential provides an efficient and accurate tool for studying LPS materials.
  • This approach enables exploration of structure-property relationships crucial for designing advanced solid-state electrolytes.
  • The GAP methodology offers a scalable pathway for accelerating the discovery and optimization of next-generation battery materials.