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Volume 64, Issue 2
  • ISSN: 2056-5135
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  • oa Structure Prediction Methods for Battery Materials

    A review of recent computational efforts to predict the atomic level structure and bonding in materials for rechargeable batteries

  • Authors: Angela F. Harper1, Matthew L. Evans1, James P. Darby1, Bora Karasulu1, Can P. Koçer1, Joseph R. Nelson2,3 and Andrew J. Morris4
  • Affiliations: 1 Department of Physics, Cavendish Laboratory, University of CambridgeJ. J. Thomson Avenue, Cambridge, CB3 0HEUK 2 Department of Materials Science and Metallurgy, University of Cambridge27 Charles Babbage Road, Cambridge, CB3 0FSUK 3 Advanced Institute for Materials Research, Tohoku University2-1-1 Katahira, Aoba, Sendai 980-8577Japan 4 School of Metallurgy and Materials, University of BirminghamEdgbaston, Birmingham, B15 2TTUK
  • Source: Johnson Matthey Technology Review, Volume 64, Issue 2, Apr 2020, p. 103 - 118
  • DOI: https://doi.org/10.1595/205651320X15742491027978
    • Published online: 01 Jan 2020

Abstract

Portable electronic devices, electric vehicles and stationary energy storage applications, which encourage carbon-neutral energy alternatives, are driving demand for batteries that have concurrently higher energy densities, faster charging rates, safer operation and lower prices. These demands can no longer be met by incrementally improving existing technologies but require the discovery of new materials with exceptional properties. Experimental materials discovery is both expensive and time consuming: before the efficacy of a new battery material can be assessed, its synthesis and stability must be well-understood. Computational materials modelling can expedite this process by predicting novel materials, both in stand-alone theoretical calculations and in tandem with experiments. In this review, we describe a materials discovery framework based on density functional theory (DFT) to predict the properties of electrode and solid-electrolyte materials and validate these predictions experimentally. First, we discuss crystal structure prediction using the random structure searching (AIRSS) method. Next, we describe how DFT results allow us to predict which phases form during electrode cycling, as well as the electrode voltage profile and maximum theoretical capacity. We go on to explain how DFT can be used to simulate experimentally measurable properties such as nuclear magnetic resonance (NMR) spectra and ionic conductivities. We illustrate the described workflow with multiple experimentally validated examples: materials for lithium-ion and sodium-ion anodes and lithium-ion solid electrolytes. These examples highlight the power of combining computation with experiment to advance battery materials research.

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Ab initio Structure Prediction Methods for Battery Materials
Johnson Matthey Technology Review 64, 103 (2020); https://doi.org/10.1595/205651320X15742491027978
/content/journals/10.1595/205651320X15742491027978
/content/journals/10.1595/205651320X15742491027978
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