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Data-driven search for promising intercalating ions and layered materials for metal-ion batteries

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Abstract

The rise in demand for lithium-ion batteries has led to a large-scale search for electrode materials and intercalating ion species to meet the demands of next-generation energy technologies. Recent efforts largely focus on searching for cathodes that can accommodate large amounts of intercalating ions, but similar work on anodes is relatively limited. This study utilizes machine learning methods to find alternative two-dimensional (2D) materials and intercalating ions beyond Li for metal-ion batteries with high-power efficiencies. The approach first uses density functional theory (DFT) calculations to estimate the theoretical capacities and voltages of various metal ions on 2D materials. The DFT-generated data also provide insights into the local structural accommodation upon ion intercalation on various 2D materials. Significant changes to the lattice can result in irreversible changes to the bonding environments in the anode material, resulting in poor cycling stability. Next, this study develops a binding energy and structural accommodation-based classification model to screen anode materials for next-generation batteries. The classification model selects intercalating ions and 2D material pairs suitable for batteries based on the calculated voltage and volumetric changes in the 2D material upon intercalation. Finally, this study builds a regression model to accurately predict the binding energies of the various intercalating ions on 2D materials. The approach highlights the importance of different elemental and structural features for classification and regression tasks. The insights gained from this study on the role of involved features, such as electronegativities of the constituent ions and the presence of unfilled electronic levels, will help to streamline further studies towards the search for future layered battery materials.

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The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work is supported by the National Science Foundation (NSF) under Grant No. DMR-1820565. The authors acknowledge support from CINT through user proposals. CINT is the Center for Integrated Nanotechnology, which is a User Facility supported by the DOE at Sandia and Los Alamos National Laboratories. The authors would also like to acknowledge the High-Performance Computing (HPC) facilities at UConn for providing resources required to carry out this work.

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  1. A. Mishra

    Present address: Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, USA

Authors and Affiliations

  1. Department of Materials Science and Engineering, University of Connecticut, Storrs, CT, USA

    S. Parida, A. Mishra & A. M. Dongare

  2. Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA

    Q. Yang

  3. Eagle Pitcher Technologies, Providence, RI, USA

    A. Dobley

  4. Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT, USA

    C. Barry Carter

  5. Center for Integrated Nanotechnologies (CINT), Sandia National Laboratories, Albuquerque, NM, USA

    C. Barry Carter

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Parida, S., Mishra, A., Yang, Q. et al. Data-driven search for promising intercalating ions and layered materials for metal-ion batteries. J Mater Sci 59, 932–949 (2024). https://doi.org/10.1007/s10853-023-09215-7

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