Ceria nanocubes have been doped with increasing amounts of lanthanum to enhance their redox ability.

Computational modelling is a vital tool in the research of batteries and their component materials. Atomistic models are key to building truly physics-based models of batteries and form the foundation of the multiscale modelling chain, leading to more robust and predictive models. These models can be applied to fundamental research questions with high predictive accuracy. For example, they can be used to predict new behaviour not currently accessible by experiment, for reasons of cost, safety, or throughput. Atomistic models are useful for quantifying and evaluating trends in experimental data, explaining structure-property relationships, and informing materials design strategies and libraries. In this review, we showcase the most prominent atomistic modelling methods and their application to electrode materials, liquid and solid electrolyte materials, and their interfaces, highlighting the diverse range of battery properties that can be investigated. Furthermore, we link atomistic modelling to experimental data and higher scale models such as continuum and control models. We also provide a critical discussion on the outlook of these materials and the main challenges for future battery research.

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First-generation cathodes for commercial lithium-ion batteries are based on layered transition-metal oxides. Research on ternary compounds, such as LiCoO2, evolved into mixed-metal systems, notably Li­(Ni,Mn,Co)­O2 (NMCs), which allows significant tuning of the physical properties. Despite their widespread application in commercial devices, the fundamental understanding of NMCs is incomplete. Here, we review the latest insights from multiscale modeling, bridging between the redox phenomena that occur at an atomistic level to the transport of ions and electrons across an operating device. We discuss changes in the electronic and vibrational structures through the NMC compositional space and how these link to continuum models of electrochemical charge–discharge cycling. Finally, we outline the remaining challenges for predictive models of high-performance batteries, including capturing the relevant device bottlenecks and chemical degradation processes, such as oxygen evolution.

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