High Accuracy Computational Methods For Lithium Ion Battery Materials

The ongoing research to improve the performance of Lithium-ion batteries has required the study of increasingly complex physical and chemical phenomena. In this context, the use of computational tools to quantitatively assess these phenomena has proven crucial for advancing the Lithium-ion battery technology. However, recent areas of research, ranging from studying the di↵diffusion of Lithium ions across solid polymer or ionic salt electrolytes, to the calculation of the voltage curve and discharge rate for complex transition metal oxide electrodes, has pushed Lithium-ion battery research beyond the framework of common computational methods, compromising the accuracy of these tools. Thus, there is an increasing need to use more accurate computational tools, or develop new ones, that could still be used in practice to design battery materials. This project presents how more accurate methods can be used to compute voltage curves for Lithium-ion cathode materials, determine the voltage stability of organic electrolyte, or predict the conductivity of di↵different electrolyte materials. The motivation for the use of higher accuracy methods is emphasized for each application by showing the limitations of commonly used methods. In particular, the achieved accuracy enables an enhanced understanding of the specific, complex physical and chemical phenomena at the heart of Lithium-ion battery limitations, which is crucial to the design of better battery materials.

Over the last two decades, lithium-based batteries have revolutionized the energy storage technologies. Li-ion batteries have found widespread use in portable electronics and electric vehicle applications. However, a detailed understanding of the battery chemistry, especially the formation of a solid electrolyte interphase (SEI)a thin passivation layer which is generated during the first charge cycle due to the reduction of electrolytesis still elusive. The mass scale commercialization of electric vehicles requires the storage capacity beyond the conventional Li-ion batteries, which spurred research interests towards Li-S technologies. Li-S batteries are attractive for their very high capacity and energy density, but their commercial application has been thwarted due to several critical limitations stemming from electrolyte dissociation chemistry and electrode material properties. To investigate the current issues associated with the Li-ion and Li-S batteries and to find possible countermeasures, we used both a newly developed computational tool eReaxFF and the standard ReaxFF reactive molecular dynamics simulations in the following research areas:1) We developed a computational method, eReaxFF, for simulating explicit electrons within the framework of the standard ReaxFF reactive force field method. We treat electrons explicitly in a pseudoclassical manner that enables simulation several orders of magnitude faster than quantum chemistry (QC) methods, while retaining the ReaxFF transferability. We describe in this thesis the fundamental concepts of the eReaxFF method, and the integration of the Atom-condensed Kohn-Sham DFT approximated to second order (ACKS2) charge calculation scheme into the eReaxFF. We trained our force field to capture electron affinities (EA) of various species. As a proof-of-principle, we performed a set of molecular dynamics (MD) simulations with an explicit electron model for representative hydrocarbon radicals. We establish a good qualitative agreement of EAs of various species with experimental data, and MD simulations with eReaxFF agree well with the corresponding Ehrenfest dynamics simulations. The standard ReaxFF parameters available in literature are transferrable to the eReaxFF method. The computationally economic eReaxFF method will be a useful tool for studying large-scale chemical and physical systems with explicit electrons as an alternative to computationally demanding QC methods. 2) A detailed understanding of the mechanism of the formation of SEI is crucial for designing high capacity and longer lifecycle lithium-ion batteries. The anode side SEI is primarily comprised of the reductive dissociation products of the electrolyte molecules. Any accurate computational method to study the reductive decomposition mechanism of electrolyte molecules is required to possess an explicit electronic degree of freedom. In this study, we employed our newly developed eReaxFF method to investigate the major reduction reaction pathways of SEI formation with ethylene carbonate (EC) based electrolytes. In the eReaxFF method, a pseudo-classical treatment of electrons provides the capability to simulate explicit electrons in a complex reactive environment. Our eReaxFF predicted simulation results of the EC decomposition reactions are in good agreement with the quantum chemistry data available in literature. Our MD simulations capture the mechanism of the reduction of the EC molecule due to the electron transfer from lithium, ring opening of the EC to generate EC-/Li+ radicals, and subsequent radical termination reactions. Our results indicate that the eReaxFF method is a useful tool for large-scale simulations to describe redox reactions occurring at electrode-electrolyte interfaces where quantum chemistry based methods are not viable due to their high computational requirement.3) Li-S batteries still suffer several formidable performance degradation issues that impede their commercial applications. The lithium negative electrode yields high anodic capacity, but it causes dendrite formation and raises safety concerns. Furthermore, the high reactivity of lithium is accountable for electrolyte decomposition. To investigate these issues and possible countermeasures, we used ReaxFF reactive molecular dynamics simulations to elucidate anode-electrolyte interfacial chemistry and utilized an ex-situ anode surface treatment with Teflon coating. In this study, we employed Li/SWCNT (single-wall carbon nanotube) composite anode instead of lithium metal and tetra (ethylene glycol) dimethyl ether (TEGDME) as electrolyte. We find that at a lithium rich environment of the anode-electrolyte interface, electrolyte dissociates and generates ethylene gas as a major reaction product, while utilization of Teflon layer suppresses the lithium reactivity and reduces electrolyte decomposition. Lithium discharge from the negative electrode is an exothermic event that creates local hot spots at the interfacial region and expedites electrolyte dissociation reaction kinetics. Usage of Teflon dampens initial heat flow and effectively reduces lithium reactivity with the electrolyte. 4) Sulfur cathodes of Li-S batteries undergo a noticeable volume variation upon cycling, which induces stress. In spite of intensive investigation of the electrochemical behavior of the lithiated sulfur compounds, their mechanical properties are not very well understood. In order to fill this gap, we developed a ReaxFF interatomic potential to describe Li-S interactions and performed MD simulations to study the structural, mechanical, and kinetic behavior of the amorphous lithiated sulfur (a-LixS) compounds. We examined the effect of lithiation on material properties such as ultimate strength, yield strength, and Youngs modulus. Our results suggest that with increasing lithium content, the strength of lithiated sulfur compounds improves, although this increment is not linear with the lithiation. The dependence of the mechanical properties and failure behavior on the loading rate of the amorphous lithiated sulfur compositions was also studied. The diffusion coefficients of both lithium and sulfur were computed for the a-LixS system at various stages of Li-loading. A Grand canonical Monte Carlo (GCMC) scheme was used to calculate the open circuit voltage (OCV) profile during cell discharge. The calculated OCV is consistent with prior experimental results. Our ReaxFF potentials also reproduced experimentally observed volume expansion of a-LixS phases upon lithiation. The Li-S binary phase diagram was constructed using genetic algorithm based tools. These simulation results provide insight into the behavior of sulfur-based cathode materials that are needed for developing high-performance lithium-sulfur batteries.

In the decade since the introduction of the first commercial lithium-ion battery research and development on virtually every aspect of the chemistry and engineering of these systems has proceeded at unprecedented levels. This book is a snapshot of the state-of-the-art and where the work is going in the near future. The book is intended not only for researchers, but also for engineers and users of lithium-ion batteries which are found in virtually every type of portable electronic product.

Lithium ion batteries have proven to be a paradigm shifting technology, enabling high energy density storage to power the handheld device and electric automotive revolutions. However relatively slow progress toward increased energy and power density has been made since the inception of the first functional lithium ion battery. Materials under consideration for next generation lithium ion batteries include anionic-redox-active cathodes, solid state electrolytes, and lithium metal anodes. Li-rich cathodes harness anionic redox, showing increased first charge capacity well beyond the redox capacity of traditional transition metal oxides, though suffer from severe capacity and voltage fade after the first cycle. This is in part attributed to oxygen evolution, driving surface reconstruction. Solid-state electrolytes (SSEs) offer the potential for safer devices, serving as physical barriers for dendrite penetration, while hoping to enable the lithium metal anode. The lithium metal naturally exhibits the highest volumetric energy density of all anode materials. Here, we employ simulation and advanced characterization methodologies to understand the fundamental properties of a variety of next generation lithium ion battery materials and devices leading to their successes or failures. Using density functional theory, the effect of cationic substitution on the propensity for oxygen evolution was explored. Improvement in Li-rich cathode performance is predicted and demonstrated through doping of 4d transition metal Mo. Next, lithium phosphorus oxynitride (LiPON), an SSE utilized in thin film batteries, was explored. LiPON has proven stable cycling against lithium metal anodes, though its stability is poorly understood. RF sputtered thin films of LiPON are examined via spectroscopic computational methods and nuclear magnetic resonance to reveal its atomic structure, ultimately responsible for its success as a thin film solid electrolyte. A new perspective on LiPON is presented, emphasizing its glassy nature and lack of long-range connectivity. Progress toward in situ methodologies for solid-state interfaces is described, and a protocol for FIB-produced nanobatteries is developed. Cryogenic methodologies are applied to a PEO/NCA composite electrode. Cryogenic focused ion beam was shown to preserve polymer structure and morphology, enabling accurate morphological quantification and preserving the crystallinity, as observed via TEM. Last, development of in situ solid-state interface characterization is discussed.

The ability to computationally predict the properties of new materials, even prior to their synthesis, has been made possible due to the current accuracy of modern ab initio techniques. In some cases, high-throughput computations can be used to create large data sets of potential compounds and their computed properties. However, regardless of the field of application, such a computational high-throughput approach faces a major problem: to be relevant, the properties need to be computed on compounds (i.e., stoichiometries and crystal structures) that will be stable enough to be synthesized. In this thesis, we address this compound prediction problem through a combination of data mining and high-throughput Density Functional Theory. We first describe a method based on correlations between crystal structure prototypes that can be used with a limited computational budget to search for new ternary oxides. In addition, for the treatment of sparser data regions such as quaternaries, a new algorithm based on the data mining of ionic substitutions is proposed and analyzed. The second part of this thesis demonstrates the application of this highthroughput ab initio computing technique to the lithium-ion battery field. Here, we describe a large-scale computational search for novel cathode materials with specific battery properties, which enables experimentalists to focus on only the most promising chemistries. Finally, to illustrate the potential of new compound computational discovery using this approach, a novel chemical class of cathode materials, the carbonophosphates, is presented along with synthesis and electrochemical results.

Abstract: Based upon advances in theoretical algorithms, modeling and simulations, and computer technologies, the rational design of materials, cells, devices, and packs in the field of lithium-ion batteries is being realized incrementally and will at some point trigger a paradigm revolution by combining calculations and experiments linked by a big shared database, enabling accelerated development of the whole industrial chain. Theory and multi-scale modeling and simulation, as supplements to experimental efforts, can help greatly to close some of the current experimental and technological gaps, as well as predict path-independent properties and help to fundamentally understand path-independent performance in multiple spatial and temporal scales.

The development of materials for clean and efficient energy generation and storage is one of the most rapidly developing, multi-disciplinary areas of contemporary science, driven primarily by concerns over global warming, diminishing fossil-fuel reserves, the need for energy security, and increasing consumer demand for portable electronics. Computational methods are now an integral and indispensable part of the materials characterisation and development process. Computational Approaches to Energy Materials presents a detailed survey of current computational techniques for the development and optimization of energy materials, outlining their strengths, limitations, and future applications. The review of techniques includes current methodologies based on electronic structure, interatomic potential and hybrid methods. The methodological components are integrated into a comprehensive survey of applications, addressing the major themes in energy research. Topics covered include: • Introduction to computational methods and approaches • Modelling materials for energy generation applications: solar energy and nuclear energy • Modelling materials for storage applications: batteries and hydrogen • Modelling materials for energy conversion applications: fuel cells, heterogeneous catalysis and solid-state lighting • Nanostructures for energy applications This full colour text is an accessible introduction for newcomers to the field, and a valuable reference source for experienced researchers working on computational techniques and their application to energy materials.

Lithium-ion batteries are ubiquitous in modern society, ranging from relatively low-power applications, such as cell phones, to very high demand applications such as electric vehicles and grid storage. The higher power and energy density of lithium-ion batteries compared to other forms of electrochemical energy storage makes them very popular in such a wide range of applications. In order to engineer improved battery design and develop better control schemes, it is important to understand internal and external battery behavior under a variety of possible operating conditions. This can be achieved using physical experiments, but those can be costly and time consuming, especially for life-studies which can take years to perform. Here using mathematical models based on porous electrode theory to study the internal behavior of lithium-ion batteries is examined. As the physical phenomena which govern battery performance are described using several nonlinear partial differential equations, simulating battery models can quickly become computationally expensive. Thus, much of this work focuses on reformulating the battery model to improve simulation efficiency, allowing for use to solve problems which require many iterations to converge (e.g. optimization), or in applications which have limited computational resources (e.g. control). Computational time is improved while maintaining accuracy by using a coordinate transformation and orthogonal collocation to reduce the number of equations which must be solved using the method of lines. Orthogonal collocation is a spectral method which approximates all dependent variables as a series solution of trial functions. This approach discretizes the spatial derivatives with higher order accuracy than standard finite difference approach. The coefficients are determined by requiring the governing equation be satisfied at specified collocation points, resulting in a system of differential algebraic equations (DAEs) which must be solved with time as the only differential variable. The system of DAEs can be solved using standard time-adaptive integrating solvers. The error and simulation time of the battery model of orthogonal collocation is analyzed. The improved computational efficiency allows for more physical phenomena to be considered in the reformulated model. Lithium-ion batteries exposed to high temperatures can lead to internal damage and capacity fade. In extreme cases this can lead to thermal runaway, a dangerous scenario in which energy is rapidly released. In the other end of the temperature spectrum, low temperatures can significantly impede performance by increasing diffusion resistance. Although accounting for thermal effects increases the computational cost, the model reformulation allows for these important phenomena to be considered in single cell as well as 2D and multicell stack battery models. The growth of the solid electrolyte interface (SEI) layer contributes to capacity fade by means of a side reaction which removes lithium from the system irreversibly as well as increasing the resistance of the transfer lithium-ion from the electrolyte to the active material. As the reaction kinetics are not well understood, several proposed mechanisms are considered and implemented into the continuum reformulated model. The effects of SEI layer growth on a lithium-ion cell over 10,000 cycles is simulated and analyzed. Furthermore, a kinetic Monte Carlo model is developed and implemented to study the heterogeneous growth of the solid electrolyte layer. This is a stochastic approach which considers lithium-ion diffusion, intercalation, and side reactions. As millions of individual time steps may be performed for a single cycle, it is very computationally expensive, but allows for simulation of surface phenomena which are ignored in continuum models.