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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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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.chemmater.0c00768.

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

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Cited By

This article is cited by 21 publications.

  1. Ganesh Sivaraman, Nicholas E. Jackson. Coarse-Grained Density Functional Theory Predictions via Deep Kernel Learning. Journal of Chemical Theory and Computation 2022, 18 (2) , 1129-1141. https://doi.org/10.1021/acs.jctc.1c01001
  2. Min Li, Susan A. Odom, Adam R. Pancoast, Lily A. Robertson, Thomas P. Vaid, Garvit Agarwal, Hieu A. Doan, Yilin Wang, T. Malsha Suduwella, Sambasiva R. Bheemireddy, Randy H. Ewoldt, Rajeev S. Assary, Lu Zhang, Matthew S. Sigman, Shelley D. Minteer. Experimental Protocols for Studying Organic Non-aqueous Redox Flow Batteries. ACS Energy Letters 2021, 6 (11) , 3932-3943. https://doi.org/10.1021/acsenergylett.1c01675
  3. Garvit Agarwal, Hieu A. Doan, Lily A. Robertson, Lu Zhang, Rajeev S. Assary. Discovery of Energy Storage Molecular Materials Using Quantum Chemistry-Guided Multiobjective Bayesian Optimization. Chemistry of Materials 2021, 33 (20) , 8133-8144. https://doi.org/10.1021/acs.chemmater.1c02040
  4. Logan Ward, Naveen Dandu, Ben Blaiszik, Badri Narayanan, Rajeev S. Assary, Paul C. Redfern, Ian Foster, Larry A. Curtiss. Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models. The Journal of Physical Chemistry A 2021, 125 (27) , 5990-5998. https://doi.org/10.1021/acs.jpca.1c01960
  5. Akshay Ajagekar, Fengqi You. Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality. Renewable and Sustainable Energy Reviews 2022, 165 , 112493. https://doi.org/10.1016/j.rser.2022.112493
  6. Malthe K. Bisbo, Bjørk Hammer. Global optimization of atomic structure enhanced by machine learning. Physical Review B 2022, 105 (24) https://doi.org/10.1103/PhysRevB.105.245404
  7. Tianyu Li, Changkun Zhang, Xianfeng Li. Machine learning for flow batteries: opportunities and challenges. Chemical Science 2022, 13 (17) , 4740-4752. https://doi.org/10.1039/D2SC00291D
  8. Morgan M Cencer, Jeffrey S Moore, Rajeev S Assary. Machine learning for polymeric materials: an introduction. Polymer International 2022, 71 (5) , 537-542. https://doi.org/10.1002/pi.6345
  9. Cheng-Han Li, Daniel P. Tabor. Discovery of lead low-potential radical candidates for organic radical polymer batteries with machine-learning-assisted virtual screening. Journal of Materials Chemistry A 2022, 10 (15) , 8273-8282. https://doi.org/10.1039/D2TA00743F
  10. Hai Qian, Michael J. Counihan, Hieu A. Doan, Nafisa A. Ibrahim, Andrew S. Danis, Worapol Setwipatanachai, Nathan S. Purwanto, Joaquín Rodríguez-López, Rajeev S. Assary, Jeffrey S. Moore. Mesolytic cleavage of homobenzylic ethers for programmable end-of-life function in redoxmers. Journal of Materials Chemistry A 2022, 10 (14) , 7739-7753. https://doi.org/10.1039/D1TA10291E
  11. Daniel Frey, Ju Hee Shin, Christopher Musco, Miguel A. Modestino. Chemically-informed data-driven optimization (ChIDDO): leveraging physical models and Bayesian learning to accelerate chemical research. Reaction Chemistry & Engineering 2022, 7 (4) , 855-865. https://doi.org/10.1039/D2RE00005A
  12. Arun Mannodi-Kanakkithodi, Xiaofeng Xiang, Laura Jacoby, Robert Biegaj, Scott T. Dunham, Daniel R. Gamelin, Maria K.Y. Chan. Universal machine learning framework for defect predictions in zinc blende semiconductors. Patterns 2022, 3 (3) , 100450. https://doi.org/10.1016/j.patter.2022.100450
  13. Mochen Liao, Kai Lan, Yuan Yao. Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework. Journal of Industrial Ecology 2022, 26 (1) , 164-182. https://doi.org/10.1111/jiec.13214
  14. Yukang Gong, Dongyu Xue, Guohui Chuai, Jing Yu, Qi Liu. DeepReac+: deep active learning for quantitative modeling of organic chemical reactions. Chemical Science 2021, 12 (43) , 14459-14472. https://doi.org/10.1039/D1SC02087K
  15. Xing-Yu Ma, Hou-Yi Lyu, Kuan-Rong Hao, Zhen-Gang Zhu, Qing-Bo Yan, Gang Su. High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials. Nanoscale 2021, 13 (35) , 14694-14704. https://doi.org/10.1039/D1NR03886A
  16. Jeanne N’Diaye, Raunaq Bagchi, Jane Y. Howe, Keryn Lian. Redox Active Organic-Carbon Composites for Capacitive Electrodes: A Review. Sustainable Chemistry 2021, 2 (3) , 407-440. https://doi.org/10.3390/suschem2030024
  17. Yunhao Xie, Yijing Liu, Renling Hu, Xu Lin, Jing Hu, Xuemei Pu. A property-oriented adaptive design framework for rapid discovery of energetic molecules based on small-scale labeled datasets. RSC Advances 2021, 11 (41) , 25764-25776. https://doi.org/10.1039/D1RA03715C
  18. Kyohei Hanaoka. Bayesian optimization for goal-oriented multi-objective inverse material design. iScience 2021, 24 (7) , 102781. https://doi.org/10.1016/j.isci.2021.102781
  19. Shijing Sun, Armi Tiihonen, Felipe Oviedo, Zhe Liu, Janak Thapa, Yicheng Zhao, Noor Titan P. Hartono, Anuj Goyal, Thomas Heumueller, Clio Batali, Alex Encinas, Jason J. Yoo, Ruipeng Li, Zekun Ren, I. Marius Peters, Christoph J. Brabec, Moungi G. Bawendi, Vladan Stevanovic, John Fisher, Tonio Buonassisi. A data fusion approach to optimize compositional stability of halide perovskites. Matter 2021, 4 (4) , 1305-1322. https://doi.org/10.1016/j.matt.2021.01.008
  20. Zhilong Wang, Haikuo Zhang, Jinjin Li. Accelerated discovery of stable spinels in energy systems via machine learning. Nano Energy 2021, 81 , 105665. https://doi.org/10.1016/j.nanoen.2020.105665
  21. Rui Hong, Peng-Fei Zhou, Bin Xi, Jie Hu, An-Chun Ji, Shi-Ju Ran. Predicting quantum potentials by deep neural network and metropolis sampling. SciPost Physics Core 2021, 4 (3) https://doi.org/10.21468/SciPostPhysCore.4.3.022

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