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Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data

Author

Listed:
  • Vishu Gupta

    (Northwestern University)

  • Kamal Choudhary

    (National Institute of Standards and Technology
    Theiss Research)

  • Francesca Tavazza

    (National Institute of Standards and Technology)

  • Carelyn Campbell

    (National Institute of Standards and Technology)

  • Wei-keng Liao

    (Northwestern University)

  • Alok Choudhary

    (Northwestern University)

  • Ankit Agrawal

    (Northwestern University)

Abstract

Artificial intelligence (AI) and machine learning (ML) have been increasingly used in materials science to build predictive models and accelerate discovery. For selected properties, availability of large databases has also facilitated application of deep learning (DL) and transfer learning (TL). However, unavailability of large datasets for a majority of properties prohibits widespread application of DL/TL. We present a cross-property deep-transfer-learning framework that leverages models trained on large datasets to build models on small datasets of different properties. We test the proposed framework on 39 computational and two experimental datasets and find that the TL models with only elemental fractions as input outperform ML/DL models trained from scratch even when they are allowed to use physical attributes as input, for 27/39 (≈ 69%) computational and both the experimental datasets. We believe that the proposed framework can be widely useful to tackle the small data challenge in applying AI/ML in materials science.

Suggested Citation

  • Vishu Gupta & Kamal Choudhary & Francesca Tavazza & Carelyn Campbell & Wei-keng Liao & Alok Choudhary & Ankit Agrawal, 2021. "Cross-property deep transfer learning framework for enhanced predictive analytics on small materials data," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26921-5
    DOI: 10.1038/s41467-021-26921-5
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    References listed on IDEAS

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    1. Dezhen Xue & Prasanna V. Balachandran & John Hogden & James Theiler & Deqing Xue & Turab Lookman, 2016. "Accelerated search for materials with targeted properties by adaptive design," Nature Communications, Nature, vol. 7(1), pages 1-9, September.
    2. Rhys E. A. Goodall & Alpha A. Lee, 2020. "Predicting materials properties without crystal structure: deep representation learning from stoichiometry," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    3. Dipendra Jha & Kamal Choudhary & Francesca Tavazza & Wei-keng Liao & Alok Choudhary & Carelyn Campbell & Ankit Agrawal, 2019. "Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    4. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
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    Cited by:

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    2. Ce Yang & Haiyan Wang & Jiaxin Bai & Tiancheng He & Huhu Cheng & Tianlei Guang & Houze Yao & Liangti Qu, 2022. "Transfer learning enhanced water-enabled electricity generation in highly oriented graphene oxide nanochannels," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Xinyu Chen & Shuaihua Lu & Qian Chen & Qionghua Zhou & Jinlan Wang, 2024. "From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning," Nature Communications, Nature, vol. 15(1), pages 1-9, December.

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