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Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs.

Shin Kiyohara1, Yu Kumagai1

  • 1Institute for Materials Research, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan.

The Journal of Physical Chemistry Letters
|May 19, 2025
PubMed
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This summary is machine-generated.

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Deep kernel learning (DKL) enhances Bayesian optimization (BO) for materials discovery by combining neural networks with Gaussian processes (GPs). DKL shows improved efficiency over standard GPs in exploring material properties, paving the way for faster material exploration.

Area of Science:

  • Materials Science
  • Computational Chemistry
  • Machine Learning

Background:

  • Machine learning (ML) is crucial for accelerating materials discovery.
  • Bayesian optimization (BO) using Gaussian processes (GPs) is a common approach for material exploration.
  • GP-based BO efficiency is limited by the need for manual feature engineering.

Purpose of the Study:

  • To investigate the effectiveness of deep kernel learning (DKL) combined with BO for materials discovery.
  • To compare the performance of DKL-based BO against standard GP-based BO.

Main Methods:

  • Application of deep kernel learning (DKL), integrating neural networks with GPs, to Bayesian optimization (BO).
  • Evaluation of DKL model efficiency on oxide datasets (922 entries) for band gaps, dielectric constants, and electron effective masses.

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  • Assessment of DKL performance on hybrid organic-inorganic perovskite alloy band gaps (610 entries) and Curie temperature prediction for 4560 alloys.

    • Main Results:

    • DKL-based BO demonstrated comparable or superior efficiency to standard GP-based BO on oxide and perovskite datasets.
    • Standard GP outperformed DKL when a strongly correlated descriptor for Curie temperature was directly usable.
    • DKL's capability for transfer learning was shown to further boost its efficiency.

    Conclusions:

    • Bayesian optimization enhanced with deep kernel learning offers a more effective approach for exploring diverse material spaces compared to standard Gaussian processes.
    • DKL addresses the feature engineering limitations of traditional GP-based BO.
    • DKL-based BO holds significant promise for accelerating the discovery of novel materials.