Computational Discovery of Solid Electrolytes for Batteries: Interfacial Phenomena and Ion Mobility

Solid-state batteries (SSBs) using a solid electrolyte (SE) and Li-metal anode are promising technologies that can increase energy density and minimize safety concerns for applications such as electric vehicles. Although the recent discovery of SEs with high ionic conductivity has advanced the prospects for realizing SSBs, additional study of these materials has unearthed several shortcomings (e.g., interfacial degradation). Thus, the discovery of alternative SE remains an important pursuit. This search has been slowed, however, by incomplete understanding of the elementary features that give rise to high ionic mobility and promote interfacial stability. In response, this dissertation focuses on several topics that are relevant for the advancement of SSBs: (1) stability and wettability at interfaces between a SE and metal anode, (2) fundamental understanding of ionic transport mechanisms in solids, and (3) the discovery of new SEs. These topics are investigated using first-principles calculations. Anti-perovskites (AP) are adopted as model SEs because they have shown promise for achieving high ionic conductivities while possessing simple structures that enable a comprehensive characterization of their properties. In addition, machine learning (ML) is employed to analyze trends in the computed data. Investigation of the Li3OCl/Li interface shows that an oxygen-terminated interface is the most stable. This interface exhibits strong interfacial bonds, suggesting good wettability by Li, low interfacial resistance, and potential for Li dendrite prevention. However, this strong interaction also locally shifts the electronic band edge positions, narrowing the bandgap by 30%. Nevertheless, the conduction band minimum remains more negative than the Li/Li+ potential, implying stability against charge injection from the anode. These calculations indicate a tradeoff between strong interfacial bonding/wettability and electrochemical stability. Next, the connections between ion mobility, thermodynamic stability, and symmetry-lowering lattice distortions are characterized across 36 model APs. Compounds with larger lattice distortions exhibit smaller percolating migration barriers because these distortions speed up migration along a subset of pathways. As larger distortions also correlate with reduced stability, realizing high ionic mobility requires balancing a mobility/stability tradeoff. Li3SeF, Na3SeF, Na3SBr, Na3SF, K3SeF, and K3SBr are identified as new compositions that balance this tradeoff. Differences in ion mobilities across Li, Na, and K based APs is rationalized in terms of differences in ion packing, vibrational frequency, polarizability, and ionic charge. Next, using data generated for alkali metal-based APs, ML is used to identify elementary features that correlate with ionic mobility. Lattice structure was found to have a greater influence in ion transport than do features based on chemical or electronic properties. For vacancy migration, the migration distance and bottleneck-size are the most important features: migration barriers decrease with shorter hops and with wider migration channels. Therefore, tuning the structure of a SE is the most effective scheme to improve ion mobility in these compounds. Finally, potential multivalent MV-ion SEs based on the AP structure are examined. SEs with compositions Mg3NAs, Ca3NAs, and Ca3PSb are identified as the most promising. These compounds are predicted to be thermodynamically stable, electronically insulating, stable in contact with metal anodes, and to have relatively low percolating barriers for ion migration. Due to their high formation energies, ionic defects should be introduced artificially. In total, this study enhances understanding of interfacial phenomena and ion transport in solid electrolytes, while also suggesting new materials. The ultimate goal is to accelerate the introduction of SSBs with improved safety and energy density.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163169/1/kwangnam_1.pd

Lattice strain causes non-radiative losses in halide perovskites

Halide perovskites are found to exhibit strain patterns over large areas, which influences the lifetimes of charge carriers.EPSR

Combinatorial Synthesis and High-Throughput Analysis of Halide Perovskite Materials for Thin-Film Optoelectronic Devices

Metallhalogenid-Perowskite (MHP) haben sich als hervorragende Materialklasse im Bereich der Optoelektronik erwiesen, obwohl die Degradation der häufig verwendeten organischen Komponenten ihre Langzeitstabilität begrenzt. Um schnell stabile Alternativen zu finden, ist eine Parallelisierung des Prozesses der Materialentwicklung durch kombinatorische Synthese und Hochdurchsatzanalyse erforderlich. In dieser Arbeit wird dies durch die Entwicklung, Implementierung und Validierung zweier komplementärer Methoden für die kombinatorische Synthese realisiert. Zum einen wurde die lösungsmittelbasierte Methode des kombinatorischen Tintenstrahldrucks weiterentwickelt, indem ein neuer Algorithmus für eine verbesserte Tintenmischung bereitgestellt und validiert wurde. Zum anderen wurde die Synthese von CsyPb1-y(BrxI1-x)2-y-Doppelgradientenschichten durch Co-Verdampfung erreicht. Kombinatorische Bibliotheken, die durch diese beiden Methoden hergestellt wurden, wurden für die Hochdurchsatzuntersuchung der strukturellen und optischen Eigenschaften der anorganischen CsyPb1-y(BrxI1-x)2-y-MHP verwendet. Dies ermöglichte die schnelle Erstellung vollständiger Phasendiagramme für Dünnfilme des CsPb(BrxI1-x)3-Mischkristalls, die zeigen, dass die Zugabe von Br die halbleitende Perowskitphase stabilisiert und niedrigere Verarbeitungstemperaturen ermöglicht. Darüber hinaus wurden CsyPb1-y(BrxI1-x)2-y-Bibliotheken mit automatisierten, kontaktlosen optischen Raster-Messungen untersucht, die eine schnelle Sichtung von über 3400 Zusammensetzungen ermöglichten. Dies ermöglichte die Bewertung des photovoltaischen Potenzials von CsyPb1-y(BrxI1-x)2-y über einen sehr breiten Bereich von Zusammensetzungen. Das höchste Wirkungsgradpotenzial wurde für stöchiometrische Zusammensetzungen gefunden, wobei ein Überschuss an Pb oder Cs zu erhöhten Verlusten durch nichtstrahlende Rekombination führt. Diese Ergebnisse liefern wichtige Erkenntnisse für die weitere Entwicklung von anorganischen MHP-Bauelementen.To keep up with the increasing need for specialized materials, a parallelization of the materials discovery process is needed through combinatorial synthesis and high-throughput analysis. The acceleration of materials discovery is especially of interest in the area of optoelectronics where metal halide perovskites (MHPs) have proven to be an excellent material class and have achieved impressive performance in photovoltaic devices among other applications. However, the degradation of the frequently employed organic components contributes to limiting the long-term stability of MHP devices. In this work, accelerated materials discovery is addressed through the development, implementation, and validation of two complementary methods for combinatorial synthesis. Firstly, the solution-based method of combinatorial inkjet printing was further developed by providing and validating a new algorithm for improved ink mixing. Secondly, the vapor-based synthesis of double-gradient CsyPb1-y(BrxI1-x)2-y was achieved by co-evaporation. Combinatorial libraries created by both methods were used for the high-throughput investigation of the structural and optical properties of the inorganic CsyPb1-y(BrxI1-x)2-y MHPs. This enabled the fast construction of complete phase diagrams for thin-films of the CsPb(BrxI1-x)3 solid solution which show that the addition of Br stabilizes the semiconducting perovskite phase and allows for lower processing temperatures. Additionally, CsyPb1-y(BrxI1-x)2-y libraries were investigated by automized, contact-less, optical mapping measurements, enabling the rapid screening of over 3400 compositions. This enabled the assessment of the photovoltaic potential of CsyPb1-y(BrxI1-x)2-y over a very broad compositional range. The maximum efficiency potential was found for stoichiometric compositions, with excess of Pb or Cs causing increased losses by non-radiative recombination. These results provide vital knowledge for further development of inorganic MHP devices

Anharmonic lattice dynamics in large thermodynamic ensembles with machine-learning force fields: the breakdown of the phonon quasiparticle picture in CsPbBr3

The harmonic approximation is a very powerful method for describing phonon dispersion relations. However, when the temperature is raised and the potential energy landscape exhibits more anharmonicity, the approximation fails to capture all crystal lattice dynamics properly. Here we study, for the first time, the phonon dispersion of a complex "Dynamic Solid" with machine-learning force fields, by simulating the dynamic structure factor (DSF) $S(\mathbf{q},\omega)$ and the projected velocity autocorrelation function (PVACF) trough large-scale molecular dynamics. These force fields have near first-principles accuracy and the linear scaling computational cost of classical potentials. To asses the strengths and weaknesses of the three methods we start with an analysis based on the classical Morse potential. Hereafter, the methods are applied to the inorganic perovskite: CsPbBr$_{3}$. This perovskite serves as an archetypal example of a wider class of novel perovskite solar-cell materials. Imaginary modes in the harmonic picture of the CsPbBr$_{3}$ structure are absent in the calculated DSF and PVACF, indicating a dynamic stabilization of the crystal. The anharmonic nature of the potential and the presence of rattling Cs$^{+}$ cations, result in the breakdown of the phonon quasi-particle picture. A more realistic picture for CsPbBr$_{3}$ emerges, presenting the phonons by a continuum of accessible frequencies.Comment: 16 pages , 11 figure

Thermal Conductivity of Complex Crystals, High Temperature Materials and Two Dimensional Layered Materials

Thermal conductivity is a critical property for designing novel functional materials for engineering applications. For applications demanding efficient thermal management like power electronics and batteries, thermal conductivity is a key parameter affecting thermal designs, stability and performances of the devices. Thermal conductivity is also the critical material metrics for applications like thermal barrier coatings (TBCs) in gas turbines and thermoelectrics (TE). Therefore, thermal conductivities of various functional materials have been investigated in the past decade, but most of the materials are simple and isotropic crystals at low temperature. This is because the first-principles calculation is limited to simple crystals at ground state and few experimental methods are only capable of measuring thermal conductivity along a single direction. The objective of this thesis is to develop first-principles based atomistic modeling tools to study thermal conductivity and phonon properties of complex crystals, high temperature materials, as well as and ultrafast laser based pump-probe techniques to characterize anisotropic thermal conductivity of layered two-dimensional materials. In the first part of this thesis, an integrated density functional theory and molecular dynamics (DFT-MD) method is developed to model the thermal conductivity and phonon properties of hybrid organic-inorganic crystals, a special kind of complex crystals integrating both organic molecules and inorganic frameworks. This DFT-MD method first develops an empirical potential field from first-principles DFT calculations, then predicts thermal conductivity using MD simulation. We applied this method to predict thermal conductivities of II-VI based hybrid crystals and organometal halide perovskites. An ultralow thermal conductivity (0.6 W/mK) is predicted in the perovskite CH3NH3PbI3, agreeing well with experimental measurements. In the second part, instead of using empirical functional forms, a data driven machine learning algorithm is used to develop high-fidelity potential field for phonon modeling. We demonstrated that the machine learning based potential is a powerful tool for modeling phonons at high temperature, even for dynamically unstable high-temperature phases, which is a challenging problem for both empirical potential based MD and static first-principles calculations. Using a simple machine learning algorithm called Gaussian process regression, we developed potential field that can effectively capture the stabilization of BCC phase of Zirconium at 1188 K, which is predicted to be unstable using static first-principles calculations. In the third part, a varied laser spot size technique based on time-domain thermoreflectance (TDTR) is developed to characterize anisotropic thermal conductivity. This method is applied to measure both the thermal conductivity parallel to the basal planes as well as the through-plane thermal conductivity of transition metal dichalcogenides, a group of layered two dimensional materials. Interestingly, the through-plane thermal conductivity is observed to decrease with the increasing heating frequency (modulation frequency of the pump laser) from 0.6 to 10 MHz, due to the non-equilibrium transport between different phonon modes. A two channel thermal model is developed to capture the non-equilibrium transport and to derive the thermal conductivity at local equilibrium. This finding suggest that in electronic devices working at a few GHz, the material could tend to become much more thermally insulating than steady state, raising great challenges for near junction thermal management.</p

Structural studies to determine the mechanisms supporting multiferroic and ferroelectric properties of complex oxides

Multiferroics are a class of materials which possess both magnetic and electrical polarization with possible coupling between them. They show promise to enable new sensors and data storage devices with novel features, such as the possibility of writing polarization bits with magnetic fields at low power. The coexisting magnetic and ferroelectric order parameters are usually weakly coupled, preventing practical use. The development and study of new classes of materials with large magnetoelectric couplings is of high importance. Understanding the structure of these materials is key to this effort. As one class of these systems, the RX3(BO3)4 has been shown to have a giant magnetoelectric (for R=Ho, X=Ai) effect of P = 0.36µC/cm2 in magnetic fields, which is significantly higher than the reported values for other multiferroic compounds. The atomic level origin is still not understood. In this work, macroscopic and atomic level properties of the full class RX3(BO3)4 (R=Ho, Gd, Eu, Sm, Nd, and X=Ai or Fe) are explored by various experimental measurements, complemented by density functional theory calculations. In HoAi3(BO3)4, an anomalous change in the structure is found in the temperature range where large magnetoelectric effects occur. No large structural change or distortion of the HoO6 polyhedra is seen to occur with a magnetic field. The magnetic field dependent structural measurements reveal enhanced structural correlation between neighboring HoO6 polyhedra. A qualitative atomic-level description of the mechanism behind the large electric polarization induced by magnetic fields in the general class of RAi3(BO1)1 systems (R= rare earth) is developed. A detailed structure related mechanism for the general RX3(BO3)4 is developed by high-resolution x-ray diffraction measurements. Another system under study is the standard class of ferroelectrics: ATiO3 including SrTiO3 and BaTiO3. The A=Sr system is known not to possess a polarization state in bulk form. In this work, pressure dependent structural measurements on monodispersed nanoscale SrTiO3 (STO) samples with average diameters of 10 to ?80 nm are conducted. A structural phase diagram of nanoscale SrTiO3 with reduced dimension is developed. A robust pressure independent polar structure is detected in the 10 nm sample for pressures up to 13 GPa while a size-dependent cubic to tetragonal transition occurs (at P = Pc) for larger particle sizes. The stability and polar characteristics of the A=Ba system are explored, and mechanisms for stabilizing the polar phase in nanoscale SrTiO3 and BaTiO3 are examined

Applications of artificial neural networks (ANNs) in several different materials research fields

PhDIn materials science, the traditional methodological framework is the identification of the composition-processing-structure-property causal pathways that link hierarchical structure to properties. However, all the properties of materials can be derived ultimately from structure and bonding, and so the properties of a material are interrelated to varying degrees. The work presented in this thesis, employed artificial neural networks (ANNs) to explore the correlations of different material properties with several examples in different fields. Those including 1) to verify and quantify known correlations between physical parameters and solid solubility of alloy systems, which were first discovered by Hume-Rothery in the 1930s. 2) To explore unknown crossproperty correlations without investigating complicated structure-property relationships, which is exemplified by i) predicting structural stability of perovskites from bond-valence based tolerance factors tBV, and predicting formability of perovskites by using A-O and B-O bond distances; ii) correlating polarizability with other properties, such as first ionization potential, melting point, heat of vaporization and specific heat capacity. 3) In the process of discovering unanticipated relationships between combination of properties of materials, ANNs were also found to be useful for highlighting unusual data points in handbooks, tables and databases that deserve to have their veracity inspected. By applying this method, massive errors in handbooks were found, and a systematic, intelligent and potentially automatic method to detect errors in handbooks is thus developed. Through presenting these four distinct examples from three aspects of ANN capability, different ways that ANNs can contribute to progress in materials science has been explored. These approaches are novel and deserve to be pursued as part of the newer methodologies that are beginning to underpin material research

Multiscale Models Of Interfacial Mechanics In Low Dimensional Systems

Crucial thrusts in modern technology from electronic information processing to engineering cellular systems require manipulation and control of materials on smaller and smaller scales to succeed. A simple and successful way to break conventional material property limitations or design multifunctional devices is to interface two different materials together. At small length scales, the surface to bulk ratio of each component material increases, to the point that the interfacial physics can dominate the properties of the engineered system. Simultaneously, the combinatorial space of possible interfaces between materials and/or molecules is far too vast to explore by trial-and-error experimentation alone. Intuitive theoretical models can greatly improve our ability to navigate such large search spaces by providing insight on how two materials are likely to interact. The goal of this thesis is to develop predictive physical models which explain emergent phenomena at material interfaces across multiple length and time scales. A variety of state-of-the-art tools were applied to realize this goal, including analytical mathematics, quantum mechanical simulations, finite element methods, and deep neural networks. At the electron scale, a continuum model parametrized by first-principles simulations was employed to develop design criteria for confined quantum states in lateral heterostructures of two-dimensional materials. At the atomic scale, a chemo-mechanical model incorporating long-range electrostatics was developed to explain synthesizability trends in composite heterostructures of inorganic perovskites and organic molecules. A machine learning graph neural network model was developed and applied to predict the impact of general surface strains on the adsorption energy of small molecule intermediates on catalyst surfaces. Finally, at the microscale, a nonlinear kinetic model was developed to explain how cells acquire and retain memory of the mechanical properties of their surroundings across multiple timescales, which can lead to irreversible adaptation and differentiation. The methods and results presented in this thesis can improve our understanding of physical phenomena arising at interfaces and provide a blueprint for future applications of multiscale computational modeling to science and engineering problems

Optical Characterisation of Hybrid Perovskites for Photovoltaic Applications

Research interest in hybrid perovskites for photovoltaic applications has accelerated rapidly over the past 10 years. Hybrid metal halide perovskites are a family of materials of the form ABX3, where A is either an organic cation or a mix of organic and inorganic cations, B is a divalent metal cation and X is a halide anion. Hybrid perovskites are promising candidates for absorbers for photovoltaic cells, as they exhibit many favourable properties such as long charge carrier lifetimes, long diffusion lengths and high charge carrier mobilities. Furthermore, the ability to fabricate thin films of such materials via solution processing means that fabricating photovoltaic cells based on hybrid perovskite is relatively inexpensive, low temperature and facile, and can be adapted for deposition on flexible substrates and for roll-to-roll processing. Hybrid perovskites which are composed of mixed A-site cations and mixed X-site halides are of particular interest due to the ability to tune the optical properties of the material through compositional engineering. The double cation, mixed halide perovskite (FAPbI3)0.85(MAPbBr3)0.15 and the triple cation, mixed halide perovskite Cs0.05FA0.76MA0.19PbI2.55Br0.45 are both excellent candidates for solar cell absorbers. In this thesis, the optical properties of these materials are investigated through a combination of spectroscopic techniques, and their crystal structure is probed through X-ray diffraction measurements. In Chapter 4, proof of concept is presented for a time-resolved PL mapping system, designed and built during this PhD project for the correlation of surface morphology with local fluorescence lifetimes in hybrid perovskite thin films. In Chapter 5, the TRPL mapping system is utilised to investigate the relationship between surface morphology and fluorescence lifetimes in (FAPbI3) 0.85(MAPbBr3)0.15 and Cs0.05 FA0.76MA0.19PbI2.55Br0.45 thin films. It is found that both types of films exhibit wrinkled morphology as a result of processing conditions, and these wrinkles correlate with local variations in PL intensity and PL lifetime. The effect of vacuum-assisted solution processing (VASP) on the morphology of spray-cast triple cation perovskites is also explored. The TRPL mapping system reveals a significant increase in surface uniformity as a result of the vacuum treatment, accompanied by lengthened fluorescence lifetimes and increased spatial ho�mogeneity of lifetimes. These findings correlated with enhanced device performance in PV cells based on VASP-treated films. In Chapter 6, absorbance and photoluminescence spectroscopy are employed to investigate the temperature dependence of the optical properties of (FAPbI3)0.85(MAPbBr3)0.15, and variable temperature X-ray diffraction measurements are taken to determine the phase behaviour of the material. Two phase transitions were identified for this material: a high temperature transition from a pseudo-cubic phase to a pseudo-tetragonal phase at ∼ 260 K (-13◦C), and a low temperature phase transition at ∼ 80 K (-193◦C) to a lower symmetry variation on the tetragonal phase. This material exhibits phase-specific optoelectronic properties, such as the discontinuity in the temperature-dependent blueshift in the optical band gap observed to correlate with the high temperature phase transition. A correlation between the size and shape of the lattice unit cell and the resulting recombination rates in the material is found, speculated to be linked to polaron formation in the material. Chapter 7 extends these investigations into the Cs-containing triple cation perovskite family of materials. In this chapter, varying amounts of Cs were added to (FAPbI3) 0.85(MAPbBr3)0.15 to determine the effect of Cs incorporation on the phase behaviour and optoelectronic properties of mixed cation perovskites. It is shown that the phase behaviour of Cs-containing triple cation perovskites is largely the same as that of (FAPbI3) 0.85(MAPbBr3)0.15, but that the high temperature cubic-tetragonal phase transition shifts to higher temperatures as Cs content is increased. The band gap of these Cs-containing perovskites widens with increasing temperature, and it is speculated that this widening is driven by the lengthening of the lattice parameter c with increasing temperature