Materials have value beyond their price. Raw materials extraction, processing and distribution embody energy and costs which are not reflected in their market value, such as irreversible ecosystem damage, use and contamination of clean drinking water and pollutants produced at every stage. Some materials require scarce or extractively costly minerals (2, 3), for instance cobalt mining involves severe toxins.

Worldwide population growth and rising demand makes sustainability a key consideration. Recycling is one way to achieve this, but it requires considerable resources and often results in low quality products. Therefore, techniques encouraging the best use of virgin materials are needed, for example advanced processing techniques producing highly functional micro- and nanostructures from smaller quantities. Multifunctionality – coupling related functions into a composite material – may increase resource efficiency across the whole supply chain.

A whole systems perspective can ensure that we are focussing on the right aspects and account for costs and impacts over a product’s lifecycle. Lightweighting and extending the lifespan of energy products are effective ways to achieve efficiencies across the entire supply chain (3).

Materials should primarily be functional at device level, fulfilling an application’s key performance indicators. However, material choice should also consider processing costs, device lifetime, environmental impacts and safety. Device performance is an inherently multiscale challenge, since material properties are strongly linked to atomistic, nanoscale and microscale structures (4), well-structured materials often performing better than their unstructured equivalents (5).

The choice of material for a particular device arises from its constituent elements and atomic structure, defining its electrical, mechanical, chemical and magnetic properties. Databases of structure-property relationships of energy materials are emerging from experimental and theoretical studies, enabling data mining for materials suited to a particular application, termed rational design. Abundant elements forming benign chemistries are indicated, for example sodium-ion batteries replacing lithium (see Titirici’s work in Edge et al. (6)).

Quantum effects and large surface area to volume ratios at the nanoscale (1–100 nm) enhance or endow new properties. Integration of one-dimensional and two-dimensional nanostructures into composites has led to significant advances, for example carbon-based nanomaterials conferring outstanding electrochemical properties and strong mechanical stability (7). Other important properties, such as porosity and mechanical strength, often rely on microstructures (100 nm to a few cm).

One route to fabricating high precision micro- and nanostructures is additive manufacturing: a range of processes building complex, three‐dimensional structures from the bottom up, with minimal waste of both materials and energy and few toxic chemicals. Other advanced, resource-efficient techniques producing complex microstructures include electrospinning and graphitising nanostructures from waste biological matter (see Cooper and Titirici’s work (6)).

Combining single function devices into systems creates unnecessary complexity in manufacturing and packaging, adding to weight and cost. Functional diversity, where coupled functions are integrated into hybrid materials, creates efficiency opportunities across the supply chain. For example, George’s work in Edge et al. (6) embeds solar cell structures into battery electrodes.

Advanced simulation capabilities speed up research into new materials and systems and allow technologies to be deployed safely and efficiently (3). Rational design’s structure databases consist largely of X-ray diffraction performed on pure crystals, while real materials are heterogeneous, for example through interfaces between components, where critical reactions occur (8) and contain a wide range of defects, such as impurities, vacancies and dislocations. The heterogeneity of materials can define their properties, for example Lucid et al. (9) looks at how to simulate grain boundaries: nanoscale interfaces in polycrystalline materials. Defects can diminish performance, but there are many materials, such as semiconductors, whose critical qualities exist because of their impurities. Understanding defects is key to enhancing material properties, for example in battery electrode materials (10). Incorporating both defects and heterogeneity into models will enable more accurate tuning of properties and performance.

Given that it is expensive to extract, process and distribute raw materials (1), particularly if they are scarce and particularly for energy devices (1), it is important that energy materials are used as effectively as possible. However, materials are subject to a range of processes throughout the supply chain, including the application of additives or coatings and packaging, all of which may exert mechanical stress, exposure and ageing. The effects of these processes are not well understood and may not have immediately detectable effects, only influencing the long-term performance. Some studies are emerging, examining the effects of processes such as calendering on battery electrodes (11). There is a need for holistic studies and the application of green chemistry principles (12) throughout the supply chain, as well as studies on degradation and its mitigation, to stretch resource usage.

A select group of researchers are profiled here, all of whom are involved in the design and characterisation of materials for electrochemical energy storage and conversion devices. These include a broad range of battery types, fuel cells, supercapacitors, photovoltaics and devices for the production, storage and utilisation of hydrogen.

Many are pioneering the use of advanced techniques for characterising energy materials, enhancing our understanding of the fundamental kinetic, structural, electronic and magnetic properties which distinguish materials as being well suited to a particular application. Some are also developing novel techniques for accurately assessing properties which are currently not easy to measure, for example: Sam Cooper and Ainara Aguadero’s work on isotopic labelling for the quantification of surface exchange and solid-state diffusivity of battery and fuel cell materials.

The performance and function of an energy material is often strongly linked to its microstructure, both in terms of its homogenised bulk properties and certain forms of heterogeneity. Understanding this link is key to enhancing manufacturing methods, through the processing of materials to component and device construction, by tailoring materials for optimum performance in the target device. Experimental techniques are complemented by computational models, providing important insights into physical and chemical processes happening at the nanoscale.

Once reliable assessment techniques are established, it will be possible to screen materials rapidly and build up a database of material properties. This high-throughput screening and a variety of machine learning tools will accelerate the identification of novel functional materials, composites and synthesis techniques for a specific purpose. A comprehensive materials library with powerful data mining capabilities can also provide diagnostics for materials from a degraded device, aiding our understanding of the mechanisms behind device ageing and failure.

Some of the research groups covered here have developed expertise in synthesising new energy materials, with provable success in controlling the resultant materials’ properties. They make use of composites, incorporating nanostructures and other exotic ingredients to introduce specific properties to an already stable and reliable base material, as well as a range of innovative techniques, such as electrospinning, to control microstructure.

The researchers presented here engage with energy research across a range of scales, from the development of atomistic mechanisms all the way up to techno-economics and policy. Beyond this, they are also all active in areas beyond energy, including sensors, catalysts and memristors, as well as the development of new experimental techniques and synthesis routes.

1. Electrochemical Energy Storage

Ainara’s current research focuses on the study and optimisation of ion-dynamics taking place in electrochemical systems, with a special focus on solid state devices, including secondary batteries, fuel cells, electrolysers and memristors. The common aim is the analysis of how the different ion dynamics affect the performance and degradation of these systems. In order to reveal this, her group uses a combination of structural, electrochemical and chemical characterisation techniques. More specifically, they use surface-sensitive analysis and isotopic labelling to reveal and differentiate different ion kinetics taking place at the bulk as well as at the surfaces and interfaces of materials.

One of the biggest topics of research focuses on development of solid state batteries, in which Ainara studies the effect of processing on lithium dynamics (8, 9) and seeks to understand the origin of dendrite formation (10, 11). Her group is also developing new isotopic labelling methods to evaluate the bulk diffusivity and surface exchange kinetics of Li in different battery materials. This work takes place in collaboration with Sam Cooper from the Dyson School of Engineering at Imperial College and will be used to correlate battery performances with variations in the Li kinetics, for instance in systems with dynamic interfaces and cation inter-diffusion processes (12).

Another important area of research is the development of fast oxygen conductors (13) and the study of the potential topotactic redox capabilities of oxides (14) and their applications for fuel cells, electrolysers, hydrogen production, memristive switching or catalysts (15). This work takes place in collaboration with John Kilner and Stephen Skinner at Imperial College London and with universities in the UK, Europe and elsewhere.

Finally, in the area of surface analysis techniques, the group has a strong background in the study of energy materials using secondary ion spectroscopy and low energy ion scattering (16). At the moment, the group is also developing a unique, worldwide facility called Hi5 (strategic equipment grant EP/P029914/1) with a plasma ion source and dual positive and negative ion detection capabilities for in situ characterisation (T, bias) of electrochemical devices, from the nm to the mm scale. Hi5 will be housed in the Department of Materials at Imperial College London.

Ainara has received funding from a number of Engineering and Physical Sciences Research Council (EPSRC) grants, the Science and Technology Facilities Council (STFC) Futures Early Career Award, Energy Cooperative Research Centre (CIC energiGUNE), the Bosch Energy Research Network and The Faraday Institution, among others.

Chandra’s research activities in the broad areas of chemistry and physics of materials seek to understand charge-carrier dynamics, ion-diffusion, charge-transport and light-matter interactions in solids and metal-organic frameworks for renewable energy. Against this backdrop, shape-controlled synthesis was successfully extended to battery materials via a colloidal route, producing phospho-olivines in the form of thin platelet crystals, which in the case of lithium iron manganese phosphate has led to a fine-tuning of metal redox energies due to cation intermixing (17) and in the case of lithium iron phosphate with an etched surface, enabled ultrafast battery charging (17). Using hierarchical carbon pre-patterned structures, ultra-flexible Li-ion battery design capable of offering fold radii down to 0.5 mm was proposed (18). By integrating solar cell materials such as organic dyes (19) and organo-halide perovskites (20) in Li-ion cell configuration, new design principles of photo-rechargeable batteries are being advanced. Lastly, by exploiting epitaxial growth relationships, bi-functional oxygen cathodes made of iron oxide nanoparticles and carbon nanotubes are shown to regulate the morphology of discharge products, enabling a fully reversible Li-air battery (21).

Current research into the development of next generation Li-ion batteries with value added features (mechanical pliability and shape-conformity) are supported by The Royal Society.

The research interests in Magda’s group are in sustainable materials, in particular porous carbon and hybrids produced from available resources such as bio- and plastic waste and abundant metals (i.e. iron, manganese and nickel). Her group produces carbon and carbon hybrids using hydrothermal processes which allow scale-up and continuous processes. They can produce up to 1 kg carbon per day and can control exactly the morphology, pore structure, pore size and shape required for each application. They have a great degree of control over the degree of graphitisation, ranging from hard carbons to soft graphitic carbons.

The group applies designer carbon materials to energy storage and conversion technologies, for example as anodes for sodium-ion batteries, electrodes in supercapacitors, cathodes in lithium-sulfur batteries and as electrocatalysts in fuel cells, electrolysers and metal-air batteries. They pay a great deal of attention to understanding the fundamentals involved in structure-function relations using advanced characterisation tools applied ex situ and operando such as: small angle X-ray spectroscopy (SAXS), small-angle neutron scattering (SANS), X-ray absorption near edge structure (XANES), transmission electron microscopy (TEM), nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI), working collaboratively with experts in these areas.

The group is well-funded and formed of around twenty researchers, with funds from EPSRC, the European Union, Innovate UK, the Royal Society, the British Council, the Royal Society of Chemistry (RSC) and industry. Their publications are highly cited and recognised internationally with 18,000 citations from 160 publications, five patents, ten book chapters and one edited book. The Principal Investigator, Professor Titirici, has been recognised internationally with the RSC Corday Morgen Prize, the IOM³ Rosenheim Medal, the Chinese Academy of Science President Award and an Honorary PhD from Stockholm University, Sweden.

Pooja’s research group, based at Loughborough University’s Department of Chemistry, focuses on computational modelling of fundamental processes in complex materials at the atomic or quantum scale. Their multiscale modelling approach combines inter-ionic potential-based methods and density functional theory (DFT) simulations in synergy with experimental groups and industry.

This requires a good understanding of the structural, electronic, magnetic and transport properties which are crucial in identifying novel functional materials for sustainable energy and catalytic applications. The nature of defects in inorganic solids as well as their effect on electronic and transport properties is also important, not only in understanding the key structure-property relationships, but also in the next phase of materials design with enhanced performance. In addition to this, a sound understanding of nano-ionic properties can yield a wealth of materials with significant technological impact.

The computational methods range from atomistic potentials-based methods, where the forces are dominated by the long-range electrostatic interactions, but also includes short range, van der Waals attractions, electron-electron repulsions and polarisability, to DFT at varying levels of theory. Molecular dynamics is also used to study the transport properties as a function of time and temperature.

Further to this, expansion towards more sophisticated time dependent density functional theory and embedded cluster methods is being pursued.

The areas of research within the group are wide-ranging with a focus on the next generation energy storage systems, thin film photovoltaics, fuel cell materials and, more uniquely, fingerprint detection materials and biomarker detection.

Pooja has received funding from several EPSRC grants and her current collaborations include: Professor Laurence Hardwick (University of Liverpool, UK); Professor David Scanlon (University College London, UK); James Cookson (Johnson Matthey Plc, UK); Professor Olle Eriksson and Biplab Sanyal (Uppsala University, Sweden); Professor Frank Tietz (Forschungszentrum Jülich, Germany) and Professor Michael Walls (Centre for Renewable Energy Systems Technology (CREST), Loughborough University, UK).

When considering the properties of crystalline materials, the impact of defects is essential. Point defects such as vacancies and dopants are the defects most commonly considered in both computational and experimental studies of material properties. Furthermore, the modelling of materials at an atomic level is often confined to bulk systems which contain these point defects (1–4). Considerably less is known about extended defects which appear in polycrystalline systems such as surfaces, dislocations and GBs. As nanostructuring of materials is becoming more prevalent, the behaviour of these extended defects is becoming significantly more important (5–8). GBs give rise to structural discontinuities within materials which result in specific structures and potential non-stoichiometry and can lead to the segregation of point defects to varying degrees, depending on the specific structure (9–13). This can significantly affect the macroscopic properties, for example: ionic conductivity, electronic conductivity, thermal conductivity, thermal expansion, elasticity and strength – all of which are crucial for many applications. Therefore, the understanding of interfaces in these materials is key to optimising their performance. Despite this, relatively little is known about the structure and even less of the effects of these interfaces on material properties due to the inherent complexity of the issues.

An example of the importance of interfaces and polycrystallinity is in the fluorite structured fast oxide ion conductors (14), such as yttria stabilised zirconia (YSZ or ZrO₂-Y₂O₃) or trivalently doped ceria (CeO₂), used in solid oxide cells, oxygen membranes and oxygen sensors (6, 15, 16). It is reported that the ionic conductivity within the GBs of these materials is several orders of magnitude lower than the bulk (17–20) with the effect attributed to a wide range of causes including impurities, dopant segregation, defect cluster formations and space charge layers (7, 8, 20–25). In contradiction it has been observed that other materials, such as Bi₂O₃ (26) and nanostructured YSZ, that the ionic conductivity is enhanced (27, 28). Much of the experimental data is based on average effects observed in impedence spectroscopy, where all GBs are treated equally as an average effect (29, 30). In fact, GBs can take on specific structures, an example of this is shown in Figure 1. That is, the atomistic description of what is happening at the GB is incomplete when obtained from macroscopic observations. Another issue which may arise when only considering average effects is that it is likely that different specifically defined interfaces will behave in different ways. As it is difficult to isolate and study the effects of GBs experimentally, computational studies are invaluable to further our knowledge of these defects and their impact on material properties.

Fig. 1.

The failure to understand the basis of material properties in polycrystalline samples is a significant impediment to the development of new materials and the application of inexpensive processing methods to existing materials. An enhanced understanding of the impact of GBs and polycrystallinity on the properties of materials would allow us to explore alternative routes to optimise their properties and ultimately enhance devices. In order to model the properties of these interfaces we first require a method for accurate prediction of interfacial structures. In this paper we present a computational method for accurately predicting the structure of low angle mirror tilt GBs which can be applied to other interfaces and even heterointerfaces. This method utilises both atomistic simulation and classical molecular dynamic simulation with sophisticated, polarisable force fields derived from ab initio data. Previous theoretical studies of GB structures generally utilise static lattice simulations with empirical force fields and structures based on experimental structures (31, 32). These results often have to be validated using first principles due to the quality and limitations of the force field. In this work the structures are predicted and validated using high-quality force fields derived from ab initio data, this is discussed further in the methodology.

Two fluorite-structured materials are investigated in this study: calcium fluoride (CaF₂) and CeO₂. CaF₂ is the prototypical fluorite material which is a super-ionic conductor at high temperatures (>1100 K) (33). CeO₂ (usually doped) is a highly technologically significant material which is both an ionic and electronic conductor with a wide range of applications including catalysis, solid oxide fuel and electrolysis cells and oxygen sensing (6, 15, 16). We compare their predicted GB structures to experimental structures from the literature obtained via transmission electron microscopy (TEM).

All GBs simulated here were generated using the minimum energy techniques applied to dislocation, interface and surface energies code (METADISE) (34). The most stable GB structures were found by carrying out optimisation scans of the GB. Surfaces with specific Miller indices were first cut and then reflected to form an interface. A potential energy surface (PES) was then calculated using a forcefield by scanning one surface relative to the other. From this scan, a two-dimensional (2D) PES for the boundary was calculated which allowed the minimum energy structure to be identified. The minimum energy GBs were then optimised and the most stable boundary was selected to investigate using molecular dynamics. The 2D potential energy scan along with the GB structure (before and after optimisation) for the Σ9(221) GB in CeO₂ is shown in Figure 2.

Fig. 2.

GBs are defined by a number of parameters: the crystallographic directions of the axes of the two grains which come together to form the interface (h_i, k_i, l_i ), the rotation axis o = (h_o, k_o, l_o ), the misorientation angle θ around the axis o and the normal axis to the GB plane n. When n is parallel to o the boundary is defined as a twist GB and when n is perpendicular to o the boundary is defined as a tilt boundary. The GBs which are studied in this work are high-angle mirror tilt GBs (n ⊥ o) and the rotation axis is (001).

The geometric definition of the GBs used in this work is the coincidence site lattice model (35). A coincidence lattice site can be defined when there exists a finite fraction of coinciding lattice sites between the two lattices (grains). This model is based on the assumption that when the energy of the GB is low, the coincidence of the atomic sites between the two grains is high, i.e. there are few bonds which are broken across the boundary. The reciprocal density of coincidence lattice sites is known as Σ and is used to characterise the geometry of the GB, as given in Equation (i):

(i)

For cubic lattices, the Σ value can be given by the sum of the squares of the Miller indices of the symmetrical tilt boundary, given by Equation (ii):

(ii)

where δ = 1 if is odd and δ = 0.5 if is even, thus in cubic systems Σ is always an odd number (35, 36). For example, the Σ9(221) GB shown above is defined by the (221) Miller index of the surfaces which are scanned to give this boundary, i.e. (2²+2²+1²) = 9, which is odd so δ = 1 and thus this is written as Σ9(221). The other GBs studied here are defined in the same way.

Initial GB structures were generated as outlined above using METADISE with shell model interaction potentials for both CaF₂ (37, 38) and CeO₂ (39). These structures were then expanded to at least 30 Ångström (Å) in the x-direction, 22 Å in the y-direction (parallel to the GB) and 76 Å in the z-direction (perpendicular to the GB). Each simulation cell contained two identical GBs as illustrated in Figure 3, with each grain having a depth of at least ~35 Å.

Fig. 3.

Molecular dynamics simulations were then carried out to determine the average GB structures. The interaction potential used for the molecular dynamics simulations is known as the dipole polarisable ion model (DIPPIM) (40), implemented in the polarisable ion model aspherical ion model (PIMAIM) code (41). The DIPPIM consists of four elements: charge-charge interactions, short-range repulsion, dispersion interactions and polarisation. This is a highly accurate, polarisable, potential, in which the dipoles are solved self consistently at each molecular dynamics step. This leads to a highly accurate description of the dipoles on ions in the simulation which is of particular importance when simulating highly polarisable ions such as F^– and O^2–. The data used to fit the DIPPIM potentials used in this work were calculated using ab initio methods (2, 42, 43). The use of ab initio data allows for non-equilibrium details on the PES to be accounted for which leads to a highly accurate, transferable interatomic potential.

Often interatomic potentials for fluorite materials are derived from equilibrium experimental data or are formed using interatomic potentials from a range of different sources resulting in inconsistent, non-transferable potentials which may have difficulties taking effects of different coordination environments into account, i.e. surfaces and interfaces. Such interatomic potentials are usually better suited to static lattice simulations as opposed to molecular dynamics simulations. In previous work on the surfaces of CeO₂ (44) we have shown that the DIPPIM provides an accurate description of extended defects and the effect of such defects on ionic transport.

The simulation cells were heated to 1473 K for 500 ps in order to simulate annealing of the GB structures, they were then cooled to 573 K for 500 ps and finally simulated at 300 K for 500 ps. Temperature scaling was carried out (at all three temperatures) every 0.025 ps before data collection for analysis began. Final GB structures were generated by averaging over the frames of the trajectory at 300 K. The DIPPIM potential parameters used for CaF₂ were previously derived by Pyper and Wilson et al. (42, 43) and those for CeO₂ were obtained by Burbano et al. (2). All steps of the simulation were carried out using the isothermal-isobaric ensemble (NPT). CaF₂ simulations utilised a timestep of 5 fs and a short-range cut-off of 14 Å and in the case of CeO₂ simulations had a timestep of 4 fs and a short-range cut-off of 11 Å. The GBs which were simulated for CaF₂ were Σ3(111), Σ5(210), Σ5(310), Σ9(221), Σ11(332), Σ13(320) and Σ13(510); and for CeO₂ are Σ3(111), Σ5(210) and Σ9(221).

Here we present the average predicted structures obtained for GBs in fluorite structured materials and compare these with TEM images obtained from experimental studies. As the F^– and O^2– ions present in CaF₂ and CeO₂ are difficult to image due to their low atomic masses we only compare the cation structures obtained with the aforementioned TEM images. First, we discuss the CaF₂ structures followed by those found for CeO₂. To the authors’ knowledge there are no experimental studies of GB structures in CaF₂ so those found in this study are compared to those of other fluorite materials (CeO₂, ZrO₂ and YSZ).

4.1 Calcium Fluoride Grain Boundaries

4.2 Ceria Grain Boundaries

We have presented a computational method for the prediction of the structure of mirror tilt GBs in fluorite structured materials. This method utilises interatomic potentials which are derived from first-principles data meaning the process is entirely predictive. The excellent level of agreement with existing experimental data on the structures of fluorite GBs highlights the power of the method. The ability to accurately predict these structures is an important first step into the computational investigation of the properties of these materials, which is key to future materials and device optimisation. The method presented here can be extended to the prediction of interfaces in different materials, interfaces of different types (i.e. twist GBs) and even heterointerfaces.