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Accelerated search for materials with targeted properties by adaptive design

Author

Listed:
  • Dezhen Xue

    (Los Alamos National Laboratory
    State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University)

  • Prasanna V. Balachandran

    (Los Alamos National Laboratory)

  • John Hogden

    (Computer and Computational Sciences, Los Alamos National Laboratory)

  • James Theiler

    (Intelligence and Space Research, Los Alamos National Laboratory)

  • Deqing Xue

    (State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University)

  • Turab Lookman

    (Los Alamos National Laboratory)

Abstract

Finding new materials with targeted properties has traditionally been guided by intuition, and trial and error. With increasing chemical complexity, the combinatorial possibilities are too large for an Edisonian approach to be practical. Here we show how an adaptive design strategy, tightly coupled with experiments, can accelerate the discovery process by sequentially identifying the next experiments or calculations, to effectively navigate the complex search space. Our strategy uses inference and global optimization to balance the trade-off between exploitation and exploration of the search space. We demonstrate this by finding very low thermal hysteresis (ΔT) NiTi-based shape memory alloys, with Ti50.0Ni46.7Cu0.8Fe2.3Pd0.2 possessing the smallest ΔT (1.84 K). We synthesize and characterize 36 predicted compositions (9 feedback loops) from a potential space of ∼800,000 compositions. Of these, 14 had smaller ΔT than any of the 22 in the original data set.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11241
    DOI: 10.1038/ncomms11241
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    Cited by:

    1. Giuseppe Forte & Federico Alberini & Mark Simmons & Hugh E. Stitt, 2021. "Use of acoustic emission in combination with machine learning: monitoring of gas–liquid mixing in stirred tanks," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 633-647, February.
    2. 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.
    3. Seeram Ramakrishna & Tong-Yi Zhang & Wen-Cong Lu & Quan Qian & Jonathan Sze Choong Low & Jeremy Heiarii Ronald Yune & Daren Zong Loong Tan & Stéphane Bressan & Stefano Sanvito & Surya R. Kalidindi, 2019. "Materials informatics," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2307-2326, August.
    4. Adarsh Dave & Jared Mitchell & Sven Burke & Hongyi Lin & Jay Whitacre & Venkatasubramanian Viswanathan, 2022. "Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Efrat Taig & Ohad Ben-Shahar, 2019. "Gradient Surfing: A New Deterministic Approach for Low-Dimensional Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 855-878, March.
    6. Loïc M Roch & Florian Häse & Christoph Kreisbeck & Teresa Tamayo-Mendoza & Lars P E Yunker & Jason E Hein & Alán Aspuru-Guzik, 2020. "ChemOS: An orchestration software to democratize autonomous discovery," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-18, April.

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