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Quantum Machine Learning in Materials Prediction: A Case Study on ABO3 Perovskite Structures.

Mosayeb Naseri1,2, Sergey Gusarov3, D R Salahub1

  • 1Department of Chemistry, Department of Physics and Astronomy, CMS - Center for Molecular Simulation, IQST - Institute for Quantum Science and Technology, Quantum Alberta, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada.

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|July 27, 2023
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Quantum machine learning (QML) effectively identifies perovskite materials using a hybrid approach. This quantum computing method shows promise for materials science discovery, even with limited data.

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Area of Science:

  • Materials Science
  • Quantum Computing
  • Machine Learning

Background:

  • Materials discovery and screening are crucial for scientific advancement.
  • Quantum machine learning (QML) presents a novel computational paradigm for complex scientific problems.
  • Identifying specific material structures, like perovskites, is essential for various applications.

Purpose of the Study:

  • To introduce and evaluate a hybrid classical-quantum machine learning model for classifying ABO3 compounds.
  • To assess the efficacy of a variational quantum classifier in identifying simple perovskite structures.
  • To demonstrate the potential of QML in materials science classification tasks with limited datasets.

Main Methods:

  • Development of a hybrid classical-quantum machine learning model.
  • Utilized a variational quantum classifier trained on a dataset of 397 ABO3 compounds.
  • Employed feature correlation analysis to optimize the QML system.

Main Results:

  • The QML system achieved an 88% accuracy on training data.
  • An accuracy of 87% was obtained on unseen test data.
  • The model successfully identified simple perovskite structures within the ABO3 dataset.

Conclusions:

  • QML, particularly hybrid approaches, shows significant potential for materials science classification.
  • The study highlights the effectiveness of QML even with limited training data.
  • Quantum computation offers enhanced capabilities for materials investigation and discovery.