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Related Concept Videos

Lattice Energies of Ionic Crystals01:27

Lattice Energies of Ionic Crystals

Lattice energy represents the energy released when gaseous cations and anions combine to form an ionic solid, reflecting the strength of electrostatic interactions within the crystal. This process is fundamentally governed by Coulombic attraction between oppositely charged ions, where the potential energy varies inversely with the interionic distance and directly with the product of ionic charges. As ions approach one another, the electrostatic energy becomes increasingly negative, indicating a...
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A type of Lewis acid-base chemistry involves the formation of a complex ion (or a coordination complex) comprising a central atom, typically a transition metal cation, surrounded by ions or molecules called ligands. These ligands can be neutral molecules like H2O or NH3, or ions such as CN− or OH−. Often, the ligands act as Lewis bases, donating a pair of electrons to the central atom. These types of Lewis acid-base reactions are examples of a broad subdiscipline called coordination...
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Ionic Bonding and Electron Transfer

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.
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:

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Optimize Before You Synthesize-Enhancing the Ionic Conductivity of Li7SiPS8 Using Bayesian Optimization.

Lucas G Balzat1,2, Robert Calaminus1,2, Yinghan Zhao3,4

  • 1Department of Nanochemistry, Max Planck Institute for Solid State Research, Stuttgart, Germany.

Angewandte Chemie (International Ed. in English)
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Bayesian optimization significantly enhanced Li7SiPS8 solid electrolyte conductivity by 350%, achieving over 7 mS/cm. This data-driven approach also reduced synthesis temperature and time, creating a more sustainable process for next-generation batteries.

Keywords:
bayesian optimizationelectrochemistryionic conductivitysolid electrolytesynthesis design

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Chemical Engineering

Background:

  • Tetragonal Li7SiPS8 is a superionic solid electrolyte with potential for batteries.
  • Its ionic conductivity is limited by amorphous side phases and unknown synthesis-performance relationships.
  • Optimizing conductivity through traditional methods is incremental and time-consuming.

Purpose of the Study:

  • To employ Bayesian optimization (BO) for efficiently increasing the ionic conductivity of Li7SiPS8.
  • To accelerate the discovery of high-performance sulfide electrolytes.
  • To develop a more energy-efficient and sustainable synthesis process.

Main Methods:

  • Bayesian optimization (BO) as a design-of-experiment approach.
  • Solid-state synthesis.
  • Characterization using quantitative Rietveld refinements, synchrotron X-ray powder diffraction, pair distribution function analysis, solid-state and pulsed-field-gradient NMR, electron microscopy, and Raman spectroscopy.

Main Results:

  • Reproducibly achieved Li7SiPS8 with ionic conductivities exceeding 7 mS/cm at room temperature (up to 350% increase).
  • Reduced synthesis temperature by 100 K (20%) and reaction time by 76 h (76%).
  • Demonstrated BO's capability to navigate complex synthesis parameter spaces.

Conclusions:

  • Bayesian optimization is an effective strategy for accelerating the development of high-performance solid electrolytes.
  • The optimized synthesis yields superior ionic conductivity and improved process efficiency.
  • This approach facilitates the creation of advanced materials for next-generation batteries.