Introduction The Royal Society of Chemistry Faraday Discussions are a series of meetings focusing on rapidly developing areas of physical chemistry. Contrary to typical conferences, Faraday Discussions rely on the active participation of speakers and audience alike. Topics for each session are based on new research papers submitted specifically for the meeting. Audience participation is...

The market for hydrogen fuel cell vehicles (FCVs) continues to grow worldwide. At present, early adopters rely on a sparse refuelling infrastructure, and there is only limited knowledge about how they evaluate the geographic arrangement of stations when they decide to get an FCV, which is an important consideration for facilitating widespread FCV diffusion. To address this, we conducted several related studies based on surveys and interviews of early FCV adopters in California, USA, and a participatory geodesign workshop with hydrogen infrastructure planning stakeholders in Connecticut, USA. From this mixed-methods research project, we distil 15 high-level findings for planning hydrogen station infrastructure to encourage FCV adoption.

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.

Introduction The Americas International Meeting on Electrochemistry and Solid State Science was a joint international conference of the 234th Meeting of The Electrochemical Society (ECS), the XXXIII Congreso de la Sociedad Mexicana de Electroquimica (SMEQ) and the 11th Meeting of the Mexico Section of the Electrochemical Society. It was well attended with worldwide representation including...