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Search for high-capacity oxygen storage materials by materials informatics.

Nobuko Ohba1, Takuro Yokoya2, Seiji Kajita1

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Researchers developed a materials informatics approach to discover new oxygen storage materials (OSMs). This method identified Cu3Nb2O8 as a superior alternative to CeO2-ZrO2 for automotive catalysts.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Catalysis

Background:

  • Oxygen storage materials (OSMs) like CeO2-ZrO2 (p-CZ) are crucial catalyst supports in automotive emission control.
  • OSMs possess oxygen storage capacity (OSC), enabling reversible oxygen release/uptake based on atmospheric conditions.

Purpose of the Study:

  • To accelerate the discovery of high-capacity OSMs using a materials informatics (MI) approach.
  • To combine experimental, first-principles calculations, and machine learning (ML) for predicting OSC.

Main Methods:

  • Measured OSC for 60 metal oxides under varying gas conditions and temperatures (973, 773, 573 K).
  • Computed descriptors based on atomic properties and first-principles calculations.
  • Trained a support vector machine regression model, selecting key features related to crystal stability and structure.

Main Results:

  • Identified features like cohesive energy as highly correlated with OSC.
  • The predictive model successfully screened 1300 existing oxides.
  • Cu3Nb2O8 was synthesized and demonstrated higher OSC than p-CZ.

Conclusions:

  • The developed MI scheme significantly accelerates the identification of novel OSMs.
  • Cu3Nb2O8 shows promise as a next-generation oxygen storage material.