Kramer, Denis

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.

The prevention of thermal runaway (TR) in lithium‐ion batteries is vital as the technology is pushed to its limit of power and energy delivery in applications such as electric vehicles. TR and the resulting fire and explosion have been responsible for several high‐profile accidents and product recalls over the past decade. Herein, the causes of TR are described and novel preventative methods are examined, approaching the problem from different angles by altering the internal structure of the battery to undergo thermal shutdown or developing the battery and thermal management systems so that they can detect and prevent TR. Ultimately, a variety of different technologies is needed to address the emerging market of highly specialized lithium‐ion batteries. Key innovations discussed include positive temperature coefficient (PTC) materials, self‐healing polymer electrolytes, and hybrid liquid–solid‐state electrolytes. Mist cooling achieves a highly uniform temperature inside the battery pack without the need for pumps to circulate a coolant. The development of battery management systems (BMSs) which model the internal temperature of the cell from real‐time data and prevent the cell reaching a critical temperature is an essential area for further research.

Cell voltage is a fundamental quantity used to monitor and control Li-ion batteries. The open cir- cuit voltage (OCV) is of particular interest as it is believed to be a thermodynamic quantity, free of kinetic effects and history and, therefore, “simple” to interpret. Here we show that the OCV characteristics of graphite show hysteresis between charge and discharge that do not solely orig- inate from Li dynamics and that the OCV is in fact history dependent. Combining First-Principles calculations with temperature-controlled electrochemical measurements, we identify a residual hysteresis that persists even at elevated temperatures of greater than 50 degC due to differences in the phase succession between charge and discharge. Experimental entropy profiling, as well as energies and volume changes determined from First-Principles calculations, suggest that the residual hysteresis is associated with different host lattice stackings of carbon and is related to Li disorder across planes in stage II configurations.

Recent research showed that the rotational degree of freedom in stacking 2D materials yields great changes in the electronic properties. Here, we focus on an often overlooked question: are twisted geometries stable and what defines their rotational energy landscape? Our simulations show how epitaxy theory breaks down in these systems, and we explain the observed behavior in terms of an interplay between flexural phonons and the interlayer coupling, governed by the moir{\'e} superlattice. Our argument, applied to the well-studied MoS2/graphene system, rationalizes experimental results and could serve as guidance to design twistronic devices.

While 2D materials attract considerable interests for their exotic electronic and mechanical properties, their phase behaviour is still largely not understood. This work focuses on (Mo:Ti) S2 binary alloys which have captured the interest of the tribology community for their good performance in solid lubrication applications and whose chemistry and crystallography is still debated. Using electronic structures calculations and statistical mechanics we predict a phase-separating behaviour for the system and trace its origin to the energetics of the d-band manifold due to crystal field splitting. Our predicted solubility limits as a function of temperature are in accordance with experimental data and demonstrate the utility of this protocol in understanding and designing TMD alloys.