Computational Material Design

A multi-scale computational framework suitable for designing solid lubricant interfaces fully in silico is presented. The approach is based on stochastic thermodynamics founded on the classical thermally activated 2D Prandtl–Tomlinson model, linked with first principles methods to accurately capture the properties of real materials. It allows investigating the energy dissipation due to friction in materials as it arises directly from their electronic structure, and naturally accessing the time-scale range of a typical friction force microscopy. This opens new possibilities for designing a broad class of material surfaces with atomically tailored properties. The multi-scale framework is applied to a class of 2D layered materials and reveals a delicate interplay between the topology of the energy landscape and dissipation that known static approaches based solely on the energy barriers fail to capture.

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.

Two-dimensional heterostructures are excellent platforms to realize twist-angle-independent ultra-low friction due to their weak interlayer van der Waals interactions and natural lattice mismatch. However, for finite-size interfaces, the effect of domain edges on the friction process remains unclear. Here we report the superlubricity phenomenon and the edge-pinning effect at MoS2/graphite and MoS2/hexagonal boron nitride van der Waals heterostructure interfaces. We found that the friction coefficients of these heterostructures are below 10−6. Molecular dynamics simulations corroborate the experiments, which highlights the contribution of edges and interface steps to friction forces. Our experiments and simulations provide more information on the sliding mechanism of finite low-dimensional structures, which is vital to understand the friction process of laminar solid lubricants.

Lithium metal plating is a critical safety issue in Li-ion cells with graphite anodes, and contributes significantly to ageing, drastically limiting the lifetime and inducing capacity loss. Nonetheless, the nucleation mechanism of metallic Li on graphite anodes is still poorly understood. But in-depth understanding is needed to rationally design mitigation measures. In this work, we conducted First-Principles studies to elucidate the Li nucleation mechanism on graphite surfaces. These large-scale density-functional-theory (DFT) calculations indicate that nano-particulate Li forms much more readily than classical nucleation theory predicts. Further, our calculations indicate a crucial role of topological surface states near the zigzag edge, lowering the nucleation barrier by a further 1.32 eV relative to nucleation on the basal plane. Li nucleation, therefore, is likely to initiate at or near the zigzag edges of graphitic particles. Finally, we suggest that chemical doping with a view to reducing the effect of the topological surface states might be a potential mitigation strategy to increase nucleation barriers and reduce the propensity to plate Li near the zigzag edge.

Progress in electrochemical technologies, such as automotive batteries, supercapacitors, and fuel cells, depends greatly on developing improved charged interfaces between electrodes and electrolytes. The rational development of such interfaces can benefit from the atomistic understanding of the materials involved by first-principles quantum mechanical simulations with Density Functional Theory (DFT). However, such simulations are typically performed on the electrode surface in the absence of its electrolyte environment and at constant charge. We have developed a new hybrid computational method combining DFT and the Poisson-Boltzmann equation (P-BE) capable of simulating experimental electrochemistry under potential control in the presence of a solvent and an electrolyte. The charged electrode is represented quantum-mechanically via linear-scaling DFT, which can model nanoscale systems with thousands of atoms and is neutralized by a counter electrolyte charge via the solution of a modified P-BE. Our approach works with the total free energy of the combined multiscale system in a grand canonical ensemble of electrons subject to a constant electrochemical potential. It is calibrated with respect to the reduction potential of common reference electrodes, such as the standard hydrogen electrode and the Li metal electrode, which is used as a reference electrode in Li-ion batteries. Our new method can be used to predict electrochemical properties under constant potential, and we demonstrate this in exemplar simulations of the differential capacitance of few-layer graphene electrodes and the charging of a graphene electrode coupled to a Li metal electrode at different voltages.

Image-based modelling has emerged as a popular method within the field of lithium-ion battery modelling due to its ability to represent the heterogeneity of the porous electrodes. A common challenge from image-based modelling is the size of 3D tomography datasets, which can be of the order of several billion voxels. Previously, different approximation methods have been used to simplify the computational problem, but each of these come with associated limitations. Here we develop a data-driven, fully parallelisable, image-based modelling framework called OpenImpala. Micro X-ray computed tomography (CT) is used to obtain 3D microstructural data from samples non-destructively. These 3D datasets are then directly used as the computational domain for finite-differences based direct physical modelling (e.g. to solve the diffusion equation directly on the CT obtained datasets). OpenImpala then calculates the equivalent homogenised transport coefficients for the given microstructure. These coefficients are written into parameterised files for direct compatibility with two popular continuum battery models: PyBamm and DandeLiion, facilitating the link between different scales of computational battery modelling. OpenImpala has been shown to scale well with an increasing number of computational cores on distributed memory architectures, making it applicable to large datasets typical of modern tomography.

In the search for alternatives to lithium batteries, aluminium makes a promising negative electrode due to its high theoretical specific energy and energy density. One battery chemistry making use of an aluminium negative electrode is the aluminium–poly(3,4-ethylenedioxythiophene) (PEDOT) battery, which has been shown to have long cycle life and specific energy comparable to other aluminium rechargeable batteries. The battery stores AlCl4− anions in the PEDOT cathode when charged. However, the storage mechanism is not well understood. Here, ab initio molecular dynamics simulations (AIMD) are used to help understand the optimum (relaxed) configuration of AlCl4− anions when stored on a single chain of PEDOT. Two main conclusions arise. Firstly, it is generally not stable to have two anions adsorbed to one monomer unit, and this configuration can be avoided for future work. Secondly, AIMD does not find lower energy configurations for the PEDOT/AlCl4− system than DFT geometry relaxation, providing that the starting geometry does not have two anions on the same monomer unit. Based on our results, we believe it is likely that similar behaviour will be observed in other conducting polymer systems.

In recent years, x-ray tomography has emerged as a powerful analytical tool for the study of lithium ion batteries and the processes occurring within. A region of specific interest is the electrode and, in particular, the heterogeneous and porous structure. The present paper is a review of studies that use x-ray tomography to characterise electrode structure, at both the cell and microstructure scales. At the cell level, x-ray tomography is used to investigate macroscopic design parameters, such as anode and cathode thicknesses, packing density and alignment of assembled cells, as well as to visualise any macroscopic structural defects, such as islanding. At the microstructure level, x-ray tomography allows for quantitative analysis of electrode structures to ascertain parameters such as particle size, tortuosity and volume fraction. The paper also explores different techniques that have been used across the field, from ex-situ, in-situ and operando techniques, to multimodal imaging methods, tomography informed design and results informed imaging.