Unsupervised landmark analysis for jump detection in molecular dynamics simulations

Molecular dynamics is a versatile and powerful method to study diffusion in solid-state ionic conductors, requiring minimal prior knowledge of equilibrium or transition states of the system's free energy surface. However, the analysis of trajectories for relevant but rare events, such as a jump of the diffusing mobile ion, is still rather cumbersome, requiring prior knowledge of the diffusive process in order to get meaningful results. In this work, we present a novel approach to detect the relevant events in a diffusive system without assuming prior information regarding the underlying process. We start from a projection of the atomic coordinates into a landmark basis to identify the dominant features in a mobile ion's environment. Subsequent clustering in landmark space enables a discretization of any trajectory into a sequence of distinct states. As a final step, the use of the smooth overlap of atomic positions descriptor allows distinguishing between different environments in a straightforward way. We apply this algorithm to ten Li-ionic systems and conduct in-depth analyses of cubic Li$_{7}$La$_{3}$Zr$_{2}$O$_{12}$, tetragonal Li$_{10}$GeP$_{2}$S$_{12}$, and the $\beta$-eucryptite LiAlSiO$_{4}$. We compare our results to existing methods, underscoring strong points, weaknesses, and insights into the diffusive behavior of the ionic conduction in the materials investigated

Electrolytes for Li-ion all-solid-state batteries: a first-principles comparative study of Li10GeP2O12 and Li10GeP2S12 in the LISICON and LGPS phases

In this work we address Li-ion diffusion in thio-LISICON materials and in their oxide counterparts, exploring both the orthorhombic and tetragonal phases of Li10GeP2S12(LGPS) and Li10GeP2O12(LGPO) through extended Car-Parrinello molecular dynamics in the canonical and isobaric-isothermal ensemble. The (quasi-)orthorhombic and tetragonal phases are studied both for the oxide and for the sulfide, with the aim of comparing their conductivity with the same approach; out of these four case studies, tetragonal LGPO has not been reported and, while dynamically stable, it sits (0.04 Ha/formula unit) above orthorhombic LGPO. We calculate activation energies for diffusion of 0.18 eV and 0.23 eV for tetragonal and orthorhombic LGPS, and of 0.22 eV and 0.34eV for tetragonal and orthorhombic LGPO. In line with experiments, we find orthorhombic LGPO orders of magnitude less conductive, at room temperature, than the two sulfide systems. However, this is not the case for tetragonal LGPO, which, although less stable than its orthorhombic allotrope, shows at room temperature a conductivity comparable to orthorhombic and tetragonal LGPS, and, if synthesized, could make a very attractive Li-ion conductor.Comment: 18 pages, 12 figures, Supplemental Materia

Modeling, understanding, and screening fast lithium-ion conductors for solid-state electrolytes

The Li-ion batteries within the consumer electronics used in our everyday life suffer from well-known deficiencies due to the prevalent use of organic liquid electrolytes: the narrow electrochemical stability windows of the organic solvents used in these electrolytes prevent the use of high-voltage cathodes, and the flammability and volatility of the solvent molecules constitute a safety hazard. Replacing the organic liquid electrolytes with inorganic solid-state electrolytes could lead to significantly safer batteries with a higher energy density. However, most known solid-state Li-ion conductors %that could be used as electrolytes are not yet suitable for application as electrolytes, since no material satisfies the stringent requirements for safety in a high-performance battery: a wide electrochemical stability window, high mechanical stability, very low electronic mobility, and fast Li-ion conduction. Searching for materials that satisfy those requirements by means of experiments is too human-labor intensive to be done on a large scale due to the time-consuming materials synthesis and experimental characterization. Computational approaches can be easily parallelized, enabling the screening of thousands of materials to find new solid-state electrolytes for Li-ion batteries. Such a computational high-throughput screening requires an automated framework and methods that are accurate enough to predict the quantities of interest but also of sufficient computational efficiency to be applied on many materials. However, known methods to predict the Li-ion conductivity in a material are either computationally too expensive to be applied on a large scale, as is the case for first-principles molecular dynamics, or are not general enough to be performed across a wide range of materials. We present a model to calculate the Li-ion diffusion coefficient and conductivity efficiently by applying physically motivated approximations to the Hamiltonian of density-functional theory. The results obtained using this "pinball model" compare well to those from accurate first-principles molecular dynamics. This agreement provides interesting insights into the dependence of the valence electronic charge density of an ionic system on the motion of Li ions and suggests that the model can be used for screening applications. After its derivation and validation, we use the pinball model in a computational high-throughput screening to find structures with promising Li-ion diffusion. These candidate solid-state electrolytes are characterized with first-principles molecular dynamics to obtain more accurate predictions of the diffusion coefficients and pathways in these materials. The pinball model, combined with the efforts to automate molecular dynamics simulations, results in a large quantity of data stored in the form of molecular dynamics trajectories, motivating a framework to analyze these in an unsupervised manner. We describe a method to investigate the diffusion mechanism in molecular dynamics simulations by performing similarity measurements between local atomic neighborhood descriptors to detect diffusive pathways and jumps of diffusing particles in an automatic and unbiased fashion. The efforts on new methods for modeling Li-ion conductors, analyzing diffusion pathways in solid-state ionic conductors, and screening for new ceramic electrolytes are summarized in the concluding chapter, which also outlines promising possibilities for future research

High-throughput computational screening for solid-state Li-ion conductors

We present a computational screening of experimental structural repositories for fast Li-ion conductors, with the goal of finding new candidate materials for application as solid-state electrolytes in next-generation batteries. We start from similar to 1400 unique Li-containing materials, of which similar to 900 are insulators at the level of density-functional theory. For those, we calculate the diffusion coefficient in a highly automated fashion, using extensive molecular dynamics simulations on a potential energy surface (the recently published pinball model) fitted on first-principles forces. The similar to 130 most promising candidates are studied with full first-principles molecular dynamics, including an estimate of the activation barrier for the most diffusive structures. The results of the first-principles simulations of the candidate solid-state electrolytes found are discussed in detail

Impact of inorganic nanoparticles on optical properties of low refractive index waveguiding polymers

The objective of this work is to improve the optical properties of low refractive index polymers used for waveguide by introduction of inorganic nanoparticles. Copolymers of fluorinated monomers and glycidyl methacrylate are used. Introduction of SiO2 nanoparticles into polymer matrix is performed by direct mixing; copolymerization with SiO2 nanoparticles modified by monomer, and in situ sol-gel formation of SiO2 during photochemical cross-linking and annealing catalyzed by photoacid generator. It is demonstrated that nanoparticles are able to decrease thermo-optic coefficient. It is also possible to fabricate waveguiding layers by direct introduction of nanoparticles without compromising of optical propagation losses

The solid-state Li-ion conductor Li7TaO6: A combined computational and experimental study

We study the oxo-hexametallate Li7TaO6 with first-principles and classical molecular dynamics simulations, obtaining a low activation barrier for diffusion of similar to 0.29 eV and a high ionic conductivity of 5.7 x 10(-4) S cm(-1) at room temperature (300 K). We find evidence for a wide electrochemical stability window from both calculations and experiments, suggesting its viable use as a solid-state electrolyte in next-generation solid-state Li-ion batteries. To assess its applicability in an electrochemical energy storage system, we performed electrochemical impedance spectroscopy measurements on multicrystalline pellets, finding substantial ionic conductivity, if below the values predicted from simulation. We further elucidate the relationship between synthesis conditions and the observed ionic conductivity using X-ray diffraction, inductively coupled plasma optical emission spectrometry, and X-ray photoelectron spectroscopy, and study the effects of Zr and Mo doping

AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

The ever-growing availability of computing power and the sustained development of advanced computational methods have contributed much to recent scientific progress. These developments present new challenges driven by the sheer amount of calculations and data to manage. Next-generation exascale supercomputers will harden these challenges, such that automated and scalable solutions become crucial. In recent years, we have been developing AiiDA (http://www.aiida.net), a robust open-source high-throughput infrastructure addressing the challenges arising from the needs of automated workflow management and data provenance recording. Here, we introduce developments and capabilities required to reach sustained performance, with AiiDA supporting throughputs of tens of thousands processes/hour, while automatically preserving and storing the full data provenance in a relational database making it queryable and traversable, thus enabling high-performance data analytics. AiiDA's workflow language provides advanced automation, error handling features and a flexible plugin model to allow interfacing with any simulation software. The associated plugin registry enables seamless sharing of extensions, empowering a vibrant user community dedicated to making simulations more robust, user-friendly and reproducible

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