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Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials

  • Hieu A. Doan
    Hieu A. Doan
    Materials Science Division  and  Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United States
    More by Hieu A. Doan
  • Garvit Agarwal
    Garvit Agarwal
    Materials Science Division  and  Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United States
  • Hai Qian
    Hai Qian
    The Beckman Institute for Advanced Science and Technology  and  Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
    Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United States
    More by Hai Qian
  • Michael J. Counihan
    Michael J. Counihan
    The Beckman Institute for Advanced Science and Technology  and  Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
    Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United States
  • Joaquín Rodríguez-López
    Joaquín Rodríguez-López
    The Beckman Institute for Advanced Science and Technology  and  Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
    Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United States
  • Jeffrey S. Moore
    Jeffrey S. Moore
    The Beckman Institute for Advanced Science and Technology,  Department of Chemistry  and  Department of Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
    Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United States
  • , and 
  • Rajeev S. Assary*
    Rajeev S. Assary
    Materials Science Division  and  Joint Center for Energy Storage Research, Argonne National Laboratory, Lemont, Illinois 60439, United States
    *(R.S.A.) Email: [email protected]. Phone: 630-252-3536.
Cite this: Chem. Mater. 2020, 32, 15, 6338–6346
Publication Date (Web):May 28, 2020
https://doi.org/10.1021/acs.chemmater.0c00768
Copyright © 2020 American Chemical Society

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    Abstract

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    We employed density functional theory (DFT) to compute oxidation potentials of 1400 homobenzylic ether molecules to search for the ideal sustainable redoxmer design. The generated data were used to construct an active learning model based on Bayesian optimization (BO) that targets candidates with desired oxidation potentials utilizing only a minimal number of DFT calculations. The active learning model demonstrated not only significant efficiency improvement over the random selection approach but also robust capability in identifying desired candidates in an untested set of 112 000 homobenzylic ether molecules. Our findings highlight the efficacy of quantum chemistry-informed active learning to accelerate the discovery of materials with desired properties from a vast chemical space.

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    • Homobenzylic ether data sets, principle component analysis, and Gaussian process regression learning curves (PDF)

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