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On comparing the reactivity of silver and lead, it is observed that the two ionic species, Ag+ (aq) and Pb2+ (aq), show a difference in their redox reactivity towards copper: the silver ion undergoes spontaneous reduction, while the lead ion does not. This relative redox activity can be easily quantified in electrochemical cells by a property called cell potential. This property is commonly known as cell voltage in electrochemistry, and it is a measure of the energy which accompanies the charge...
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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.
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Colligative Properties of Electrolytes
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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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Coordination compounds and complexes exhibit different colors, geometries, and magnetic behavior, depending on the metal atom/ion and ligands from which they are composed. In an attempt to explain the bonding and structure of coordination complexes, Linus Pauling proposed the valence bond theory, or VBT, using the concepts of hybridization and the overlapping of the atomic orbitals. According to VBT, the central metal atom or ion (Lewis acid) hybridizes to provide empty orbitals of suitable...
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Element Code from Pseudopotential as Efficient Descriptors for a Machine Learning Model to Explore Potential

Meng-Huan Jao1,2, Shun-Hsiang Chan1,3, Ming-Chung Wu1,2,3,4

  • 1Green Technology Research Center, Chang Gung University, Taoyuan 33302, Taiwan.

The Journal of Physical Chemistry Letters
|October 6, 2020
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Summary

We developed a new descriptor, Element Code, for machine learning in material science. This descriptor accurately predicts the bandgap of lead-free perovskites, aiding in new material discovery.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Machine learning (ML) shows great promise in material science.
  • Descriptor selection is crucial for ML model performance.

Purpose of the Study:

  • Introduce novel Element Code descriptors generated from pseudopotentials.
  • Validate Element Code's ability to capture essential elemental information.
  • Utilize Element Code for predicting lead-free double halide perovskite bandgaps.

Main Methods:

  • Generated Element Code descriptors using pseudopotential data.
  • Employed a variational autoencoder for unsupervised learning to create Element Code.
  • Constructed an ML model using Element Code as the sole descriptor.

Main Results:

  • Verified that Element Code contains representative elemental information.
  • Achieved high accuracy (0.951) and low mean absolute error (0.266 eV) in bandgap prediction.
  • Demonstrated Element Code's efficacy as a primary descriptor.

Conclusions:

  • Element Code is a powerful descriptor for ML in material science.
  • This approach offers insights for selecting lead-free halide perovskites.
  • Establishes a new paradigm for exploring novel materials.