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Machine Learning-Enabled Superior Energy Storage in Ferroelectric Films with a Slush-Like Polar State

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Download Hi-Res ImageDownload to MS-PowerPointCite This:Nano Lett. 2023, 23, 11, 4807-4814
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    Machine Learning-Enabled Superior Energy Storage in Ferroelectric Films with a Slush-Like Polar State
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    • Ruihao Yuan
      Ruihao Yuan
      T-4, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
      State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an 710072, China
      More by Ruihao Yuan
    • Abinash Kumar
      Abinash Kumar
      Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
      More by Abinash Kumar
    • Shihao Zhuang
      Shihao Zhuang
      Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
      More by Shihao Zhuang
    • Nicholas Cucciniello
      Nicholas Cucciniello
      Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
      Department of Materials Design and Innovation, University at Buffalo-The State University of New York, Buffalo, New York 14260, United States
      More by Nicholas Cucciniello
      Orcidhttps://orcid.org/0000-0002-9399-4985
    • Teng Lu
      Teng Lu
      Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory 2601, Australia
      More by Teng Lu
      Orcidhttps://orcid.org/0000-0002-7866-1859
    • Deqing Xue
      Deqing Xue
      State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, China
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    • Aubrey Penn
      Aubrey Penn
      MIT.nano, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
      More by Aubrey Penn
      Orcidhttps://orcid.org/0000-0001-7586-1655
    • Alessandro R. Mazza
      Alessandro R. Mazza
      Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
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    • Quanxi Jia
      Quanxi Jia
      Department of Materials Design and Innovation, University at Buffalo-The State University of New York, Buffalo, New York 14260, United States
      More by Quanxi Jia
    • Yun Liu
      Yun Liu
      Research School of Chemistry, The Australian National University, Canberra, Australian Capital Territory 2601, Australia
      More by Yun Liu
      Orcidhttps://orcid.org/0000-0002-5404-3909
    • Dezhen Xue*
      Dezhen Xue
      State Key Laboratory for Mechanical Behavior of Materials, Xi’an Jiaotong University, Xi’an 710049, China
      *Email: [email protected]
      More by Dezhen Xue
    • Jinshan Li*
      Jinshan Li
      State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an 710072, China
      *Email: [email protected]
      More by Jinshan Li
    • Jia-Mian Hu
      Jia-Mian Hu
      Department of Materials Science and Engineering, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
      More by Jia-Mian Hu
      Orcidhttps://orcid.org/0000-0002-7579-6440
    • James M. LeBeau
      James M. LeBeau
      Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
      More by James M. LeBeau
      Orcidhttps://orcid.org/0000-0002-7726-3533
    • Aiping Chen*
      Aiping Chen
      Center for Integrated Nanotechnologies (CINT), Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States
      *Email: [email protected]
      More by Aiping Chen
      Orcidhttps://orcid.org/0000-0003-2639-2797
    Other Access OptionsSupporting Information (1)

    Nano Letters

    Cite this: Nano Lett. 2023, 23, 11, 4807–4814
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.nanolett.3c00277
    Published May 24, 2023
    Copyright © 2023 American Chemical Society
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    Abstract

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    Abstract Image

    Heterogeneities in structure and polarization have been employed to enhance the energy storage properties of ferroelectric films. The presence of nonpolar phases, however, weakens the net polarization. Here, we achieve a slush-like polar state with fine domains of different ferroelectric polar phases by narrowing the large combinatorial space of likely candidates using machine learning methods. The formation of the slush-like polar state at the nanoscale in cation-doped BaTiO3 films is simulated by phase field simulation and confirmed by aberration-corrected scanning transmission electron microscopy. The large polarization and the delayed polarization saturation lead to greatly enhanced energy density of 80 J/cm3 and transfer efficiency of 85% over a wide temperature range. Such a data-driven design recipe for a slush-like polar state is generally applicable to quickly optimize functionalities of ferroelectric materials.

    Copyright © 2023 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.nanolett.3c00277.

    • Experimental section/methods details on the film growth, STEM characterization, machine learning, and phase field simulation; confusion matrix of the classification model based on machine learning; the top ten compositions with high probability to be located on the R–T boundary; Top electrodes measured by Scanning Electron Microscope; ADF STEM images of cross-section of 0Sn and 3Sn thin films; STEM-EDS elemental maps for 3Sn thin film; in-plane and out-of-plane lattice parameters comparison; lattice parameters for 0Sn and 3Sn thin films; the temperature dependence of dielectric constant and loss tangent for the 0Sn thin film; projected displacement (polarization) map for 0Sn and 3Sn films; polarization vs electric field curves for 0Sn, 1Sn, and 5Sn films; P–E loops for bulk ceramics of 0Sn, 1Sn, 3Sn, and 5Sn; leakage current as a function of electric field at different temperatures for 3Sn film; P–E curves at temperatures ranging from −105 to 95 °C for 3Sn thin film; electron energy loss spectrum for 0Sn and 3Sn films (PDF)

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    Cited By

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    This article is cited by 8 publications.

    1. Shumao Xu, Farid Manshaii, Xiao Xiao, Jun Chen. Artificial intelligence assisted nanogenerator applications. Journal of Materials Chemistry A 2025, 13 (2) , 832-854. https://doi.org/10.1039/D4TA07127A
    2. Long Geng, Yabo Yan, Yitong Cao, Guo Li, Changhui Liu. Urea-aided phase change thermal energy storage performance regulation for thermal management. Journal of Energy Storage 2024, 104 , 114678. https://doi.org/10.1016/j.est.2024.114678
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    4. Di Zhang, Katherine J. Harmon, Michael J. Zachman, Ping Lu, Doyun Kim, Zhan Zhang, Nicholas Cucciniello, Reid Markland, Ken William Ssennyimba, Hua Zhou, Yue Cao, Matthew Brahlek, Hao Zheng, Matthew M. Schneider, Alessandro R. Mazza, Zach Hughes, Chase Somodi, Benjamin Freiman, Sarah Pooley, Sundar Kunwar, Pinku Roy, Qing Tu, Rodney J. McCabe, Aiping Chen. High‐throughput combinatorial approach expedites the synthesis of a lead‐free relaxor ferroelectric system. InfoMat 2024, 6 (9) https://doi.org/10.1002/inf2.12561
    5. A. R. Mazza, J.-Q. Yan, S. Middey, J. S. Gardner, A.-H. Chen, M. Brahlek, T. Z. Ward. Embracing disorder in quantum materials design. Applied Physics Letters 2024, 124 (23) https://doi.org/10.1063/5.0203647
    6. Bingzhong Shen, Jia‐Han Zhang, Yang Liu, Jinpeng Ma, Yong Li, Xihong Hao, Rui Zhang. Enhanced Absolute Recovered Energy under Low Electric Field in All‐Inorganic 0–3 Nanocomposition Thick Films. Small 2024, 20 (24) https://doi.org/10.1002/smll.202309486
    7. Jiangheng Jia, Zhizhan Dai, Song Ding, Yiwei Wang, Shengchun Shen, Ying Hou, Yuewei Yin, Xiaoguang Li. Enhancing energy storage performance of polyethylene via passivation with oxygen atoms through C–H vacancy carbonylation. Materials Today Energy 2024, 42 , 101553. https://doi.org/10.1016/j.mtener.2024.101553
    8. Nicholas Cucciniello, Alessandro R. Mazza, Pinku Roy, Sundar Kunwar, Di Zhang, Henry Y. Feng, Katrina Arsky, Aiping Chen, Quanxi Jia. Anisotropic Properties of Epitaxial Ferroelectric Lead-Free 0.5[Ba(Ti0.8Zr0.2)O3]-0.5(Ba0.7Ca0.3)TiO3 Films. Materials 2023, 16 (20) , 6671. https://doi.org/10.3390/ma16206671
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    Nano Letters

    Cite this: Nano Lett. 2023, 23, 11, 4807–4814
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.nanolett.3c00277
    Published May 24, 2023
    Copyright © 2023 American Chemical Society
    Request reuse permissions

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