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 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.
Accelerating cathode material discovery through ab initio random structure searching
