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Whether solid, liquid, or gas, a substance's state depends on the order and arrangement of its particles (atoms, molecules, or ions). Particles in the solid pack closely together, generally in a pattern. The particles vibrate about their fixed positions but do not move or squeeze past their neighbors. In liquids, although the particles are closely spaced, they are randomly arranged. The position of the particles are not fixed—that is, they are free to move past their neighbors to...
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A phase transition is the process in which a substance changes from one state of matter to another, like from a solid to a liquid, liquid to gas, or vice versa, at a specific temperature and under given pressure conditions. This change is spontaneous and is affected by alterations in temperature and pressure. These parameters impact the strength of the forces between molecules (intermolecular forces) in the substance.During a phase transition, both the initial and final phases of the substance...
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Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning.

Dezhen Xue1,2, Prasanna V Balachandran1, Ruihao Yuan2

  • 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545.

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|November 9, 2016
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Summary
This summary is machine-generated.

This study introduces a Bayesian approach for materials informatics, successfully predicting and synthesizing a new piezoelectric material with enhanced temperature reliability for ferroelectric applications.

Keywords:
Bayesian learningPb-free materialsmaterials informaticsmorphotropic phase boundarypiezoelectric materials

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Solid-State Physics

Background:

  • Materials informatics requires integrating domain knowledge into Bayesian frameworks for efficient materials discovery.
  • Discovering new ferroelectric materials with stable piezoelectric properties across varying temperatures is crucial for technological advancement.

Purpose of the Study:

  • To develop and demonstrate a Bayesian approach for materials discovery, incorporating materials knowledge to guide the search for new piezoelectrics.
  • To identify and synthesize a BaTiO3-based solid solution exhibiting a vertical morphotropic phase boundary and improved temperature reliability.

Main Methods:

  • Utilized phase diagrams, ferroelectric response descriptors, and Landau-Devonshire theory as inputs for a Bayesian model.
  • Employed computational prediction followed by experimental synthesis and characterization of the target material.
  • Evaluated the piezoelectric properties and temperature stability of the synthesized solid solution.

Main Results:

  • Successfully predicted and synthesized the (Ba0.5Ca0.5)TiO3-Ba(Ti0.7Zr0.3)O3 solid solution.
  • The new material exhibits piezoelectric properties with superior temperature reliability compared to existing BaTiO3-based materials in the training dataset.
  • Demonstrated the efficacy of the Bayesian approach in guiding the discovery of functional materials.

Conclusions:

  • The developed Bayesian framework effectively integrates materials knowledge for targeted materials discovery.
  • The synthesized (Ba0.5Ca0.5)TiO3-Ba(Ti0.7Zr0.3)O3 offers a promising lead for next-generation piezoelectric applications requiring high-temperature stability.
  • This work highlights the potential of combining computational and experimental approaches for accelerating materials innovation.