Ab initio 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
Johnson Matthey Technology Review
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Harper, A., Evans, M., Darby, J., Karasulu, B., Koçer, C., Nelson, J., & Morris, A. (2020). Ab initio 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. Johnson Matthey Technology Review, 64 (2), 103-118. https://doi.org/10.1595/205651320x15742491027978
© 2020 Johnson Matthey 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 standalone 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 ab initio 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.
(1) Gates Cambridge Trust, University of Cambridge, UK (2) EPSRC Centre for Doctoral Training in Computational Methods for Materials Science, UK, Grant No. EP/L015552/1. (3) Winton Programme for the Physics of Sustainability, University of Cambridge, UK (4) Sims Fund, University of Cambridge, UK (5) EPSRC Grant No. EP/P003532/1 (6) EPSRC Collaborative Computational Projects on the Electronic Structure of Condensed Matter (CCP9), Grant No. EP/M022595/1, and NMR crystallography, Grant No. EP/M022501/1 (7) Computing resources on the Tier 1 resource ARCHER were provided through the UKCP EPSRC High-End computational consortium (EP/P022561/1) and on the Tier 2 resources HPC Midlands+ (EP/P020232/1) and CSD3 (EP/P020259/1).
Isaac Newton Trust (16.24(q))
External DOI: https://doi.org/10.1595/205651320x15742491027978
This record's URL: https://www.repository.cam.ac.uk/handle/1810/303824
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/