The formation of dendrites on the anode surface of metal air batteries, such as the Li-air battery, causes significant decreases in performance over the lifetime of the battery and poses safety concerns due to short circuiting. Predictive computational methods are used to investigate novel electrolyte materials which will reduce dendrite growth, in particular liquid crystal materials for Li-air electrolytes. The literature on liquid crystal electrolytes was surveyed for materials data and used to develop a training set of liquid crystal compounds. These compounds were then used as a knowledge base to develop property structure relations for the clearing and melting temperature for liquid crystals, which are a critical design parameter for the design of novel electrolytes. This was accomplished using standard artificial neural networks trained on molecular fingerprints and convolutional neural networks (CNNs) trained on compound images. Transfer learning was also demonstrated to boost predictive performance through pre-training neural networks on larger pre-existing compound datasets. The results show that CNNs achieve comparable accuracy compared to molecular fingerprints, and that both multilayer perceptrons and CNNs can benefit from transfer learning. This study is the first where transfer learning in CNNs aids in the prediction of one experimental property in a small data regime, using data of another experimental property.

High Energy density lithium (Li) batteries are promising advanced energy technologies for applications in the automotive and portable power markets. Advanced Li batteries have several design and operational challenges to overcome before becoming commercially viable. One of these challenges is the formation of dendrites on the anode surface of the Li-air battery over multiple charge and discharge cycles. Dendrite formation decreases cell performance and causes safety concerns due to short circuiting. In this research computational methods are adopted to investigate the physics of dendrite formation in Li-air batteries and to develop strategies to suppress dendrite growth. The effects of convection and anisotropic transport properties in the electrolyte on the morphology and growth rate of dendrites are investigated using a two-dimensional (2D) smoothed particle hydrodynamics (SPH) model. SPH is a mesh-free Lagrangian computational fluid dynamics method, which allows for easy implementation of complex physics at the dendrite surface. Anisotropic diffusion is modeled to study the effects of mixing near the anode-electrolyte interface on dendrite growth. The simulation results suggest that high anisotropy in the electrolyte promotes a robust, compact dendrite structure versus isotropic diffusion cases. Convection effects are also considered to study the electro-osmosis effect on dendrite growth. Circulating flows are included in the model to simulate electro-osmotic flows near the anode-electrolyte interface. The simulation results reveal that the convection effect will increase the mixing near the dendrite and lead to significant changes in dendrite morphology. The results of these computational studies are being used to develop mitigation strategies for dendrite growth in Li-air batteries. These strategies include the use of novel electrolyte solutions and hybrid electrolyte designs, which allow for the tuning of transport properties through the electrolyte. The computational models are being used to investigate these designs and optimize material properties to increase battery performance and lifetime. Figure 1

This research focuses on improving the performance and durability of advanced lithium air (Li-air) battery technologies through computational modeling and materials informatics. In particular, the issue of dendrite growth at the anode-electrolyte interface was investigated through physical models and a materials informatics approach to identify novel electrolyte materials for Li-air batteries. Dendrites form on the anode surface over multiple charge and discharge cycles, causing a decrease in performance and safety concerns due to short circuiting. Dendrite growth depends strongly on the reactive transport near the anode-electrolyte interface and heterogeneities on the anode surface. To reduce dendrite growth, we investigated increasing the mixing near the anode-electrolyte interface through novel electrolyte designs that directionally affect the transport properties of the electrolyte solution. The research was divided into two main tasks: physical modeling of dendrite growth and morphology, and development of a materials informatics approach for identifying novel electrolyte materials. Dendrite growth was investigated using the meso-scale smoothed particle hydrodynamics (SPH) method. SPH is a mesh-free technique that uses a Lagrangian framework to model the simulation domain as a discrete system of particles. SPH particles are used as interpolation points to discretize and solve the governing partial differential equations of a system based on the SPH smoothing function. Due to its Lagrangian nature, SPH does not require explicit boundary tracking, which allows for simple implementation of complex geometries and moving boundaries, such as the dendrite formation at the anode-electrolyte interface. A materials informatics approach was used in a discovery mode to identify promising material combinations for the electrolyte. Our research focused on developing the materials informatics framework for electrolyte solutions. The developed framework is general and is applicable to other materials systems for batteries and for other application areas. The informatics techniques have predictive capabilities based upon an electrolyte materials knowledge base, allowing us to evaluate materials based on their thermodynamic properties and the desired properties needed for a specific application.

While carrier transport properties are critical to semiconductor efficiency, estimations for new materials based upon prior mobility measurements can be problematic. As with all new-materials screens, carrier transport screens must be based only on properties readily available prior to synthesis, such as composition. For semiconducting radiation detectors, transport is characterized by the mu-tau product and its carrier mobility (mu) and lifetime (tau) components. Because the time to pure-material synthesis is generally long, and due to the associated problems with fully-characterizing impure and defect-containing early-stage materials, it is advantageous to consider “ultimate” properties appropriate to the projected performance of a more advanced material. Here, ultimate properties and their application to materials screening of electron mobilities of semiconductors is discussed within the context of optical polaron scattering. The use of ultimate properties for electron mobility and lifetime in screening semiconducting radiation detectors is assessed to determine whether required inputs for electron mobility and carrier lifetime are likely to be accessible to screenable form for new-materials.

In this paper we investigate transport limitations in the electrodes of lithium-air batteries through computational modeling. We use meso-scale models to consider the effects of dendrites on the current and potential at the anode surface, and to investigate the effects of reaction and transport parameters on the formation of precipitates in the cathode. The formation of dendrites on the anode surface during cycling reduces the transport of ions and can lead to short circuits in the cell. Growth of precipitates in the cathode reduces the specific capacity of the cell due to surface passivation and pore clogging. Both of these degradation mechanisms depend on meso-scale phenomena, such as the pore-scale reactive transport in the cathode. To understand the effects of the meso-scale transport and precipitation on the performance and lifetime of Li-air batteries, meso-scale modeling is needed that is able to resolve the electrodes and their microstructures.

MnBi has gained much attention as a replacement for critical rare earth magnetic material not only due to its strong magnetization and coercive power, but also because of its capability to retain magnetization at elevated temperatures while most other compounds decline. To investigate the origin of this temperature dependence, we have performed a series of first principles electronic structure calculations on the thermomagnetic properties of MnBi and compared it with MnSb , another ferromagnetic material with a strong magnetic energy product, same crystal structure at room temperature and similar Curie temperature. Three structural phases were considered in this study: NiAs -type ( B8 1 ), MnP -type ( B31 ) and a zincblende-type ( B3 ) structures. Calculated magnetizations demonstrated structural effects on temperature dependent magnetization. For the same NiAs -type structure, MnBi has a monotonic increase in magnetization with increasing temperature while MnSb decreases. In the other two structures, magnetization in MnBi and MnSb are much less sensitive to temperature. Results from this study suggest a structural design rule for the development of new MnBi related materials.

The electronic structure of organic and inorganic polymeric systems are well described in terms of their molecular symmetry, even with the large bond polarity shown by such systems as polyphosphazenes. We have performed calculations using the semi-empirical CNDO/1 method to determine the valence electronic structure for a series of model phosphonitrilic and organic compounds. The optical transition energies for phosphonitrilic compounds are greater than their organic counterparts as a result of in-plane π’ bonding interactions. The extent of these interactions is modulated by the electronegativity of the substituent groups on the phosphorus atoms. We report values for the vertical ionization energy and electronic absorption wavelengths, and use molecular orbital contour analysis to show the effects of ligand electronegativity on the π’ network.

Optical properties and durability of thin films are influenced by strain which can be evaluated from frequency shifts of the lattice phonon lines in measured Raman spectra. The response of titania samples to applied pressure is reported in this work. Anatase and rutile samples of thin films (sol-gel and sputter deposited) and bulk materials have been subjected to hydrostatic pressures approaching 100 kbar in a diamond anvil cell. Results indicate that the rutile samples exhibit similar responses to applied pressure. Anatase sol-gel films exhibit a pressure-dependent response that suggests that the sol-gel film is more compressible than the bulk material, and a pressure-induced phase transformation observed for the bulk material is inhibited in the anatase sol-gel film. The anomalous pressure response of the anatase sol-gel film may result from the film microstructure which has been shown by transmission electron microscopy to consist of spheres of crystalline TiO2 surrounded by microscopic voids.

We develop a new dimension reduction method for large size systems of ordinary differential equations (ODEs) obtained from a discretization of partial differential equations of viscous single and multiphase fluid flow. The method is also applicable to other large-size classical particle systems with negligibly small variations of particle concentration. We propose a new computational closure for mesoscale balance equations based on numerical iterative deconvolution. To illustrate the computational advantages of the proposed reduction method, we use it to solve a system of smoothed particle hydrodynamic ODEs describing single-phase and two-phase layered Poiseuille flows driven by uniform and periodic (in space) body forces. For the single-phase Poiseuille flow driven by the uniform force, the coarse solution was obtained with the zero-order deconvolution. For the single-phase flow driven by the periodic body force and for the two-phase flows, the higher-order (the first- and second-order) deconvolutions were necessary to obtain a sufficiently accurate solution.

Materials repositories increasingly provide data support for building predictive structure-processing/property models. To this end, an effective materials property database requires a substantial degree of data completeness, which considers factors including the range and density of property values, and the ability of system identifiers to unambiguously reference distinct physical systems. Repositories show great promise as design tools, although information gaps in data coverage can be identified. Data completeness issues include: 1) incompletely specified alloy systems and system definitions that do not fully resolve property values; and 2) reported magnesium alloy systems focus on a relatively few common alloys such as the AZ alloy series. Nonetheless current information is sufficient to draw some conclusions for magnesium alloy design, and suggest design hypotheses. An iterative process to improve the predictive capability of repositories would 1) enhancing the system definition ( including more detailed processing and post-treatment information), adding more mechanical measurements for "new" magnesium alloys, and testing hypotheses identified from current information.

Informatics-based identification of candidate semiconducting radiation detection materials depends upon the development of a robust knowledge base of materials properties. However, the accuracy and integrity of the knowledge base are often affected by information loss due to incomplete entry and loss of context. We describe our methods for materials property data storage and retrieval, in support of semiconductor development for gamma radiation detection materials informatics applications. Analysis-ready data representations vary with each materials design problem, and are often inconsistent with accurate generic property storage. The proposed approach provides simple, strongly-typed generic storage for as-measured properties, with tools for assessing as-measured properties and converting them to analysis-ready representations. This process simplifies property data stewardship, and allows fine control over the assumptions of data fusion, system characterization, and property representation employed in property-estimation models.

A Lagrangian particle model for multiphase multicomponent fluid flow, based on smoothed particle hydrodynamics (SPH), was developed and used to simulate the flow of an emulsion consisting of bubbles of a non-wetting liquid surrounded by a wetting liquid. In SPH simulations, fluids are represented by sets of particles that are used as discretization points to solve the Navier–Stokes fluid dynamics equations. In the multiphase multicomponent SPH model, a modified van der Waals equation of state is used to close the system of flow equations. The combination of the momentum conservation equation with the van der Waals equation of state results in a particle equation of motion in which the total force acting on each particle consists of many-body repulsive and viscous forces, two-body (particle–particle) attractive forces, and body forces such as gravitational forces. Similar to molecular dynamics, for a given fluid component the combination of repulsive and attractive forces causes phase separation. The surface tension at liquid–liquid interfaces is imposed through component dependent attractive forces. The wetting behavior of the fluids is controlled by phase dependent attractive interactions between the fluid particles and stationary particles that represent the solid phase. The dynamics of fluids away from the interface is governed by purely hydrodynamic forces. Comparison with analytical solutions for static conditions and relatively simple flows demonstrates the accuracy of the SPH model.

Materials properties important to the design and performance of semiconducting gamma detectors, such as band gap, density, mobility, and crystal cell anisotropy, can depend on similar underlying physics. The resulting property correlations limit the number of design variable and the place effective bounds on the range of physical properties available to gamma-detection materials However, trend correlations can also limit the dependence of error in structure-property relationships and information gaps when considering new candidate materials. Trend analysis complements property estimation via data regression techniques, increasing the generality and certainty of information-based conclusions.

An information-based approach to scintillating materials development has been applied to ranking the alkali halide and alkaline earth halide series in terms of their energy conversion efficiency. The efficiency of scintillating radiation detection materials can be viewed as the product of a consecutive series of electronic processes (energy conversion, transfer, and luminescence) as outlined by Lempicki and others. Relevant data are relatively sparse, but sufficient for the development of forward mapping of materials properties through materials signatures. These mappings have been used to explore the limits of the branching ratio between the ionization and phonons (K) in the Lempicki model with chemical composition, and examine its relationship with another common design objective, density. The alkali halides and alkaline earth halide compounds separate themselves into distinct behavior classes favoring heavier cations and anions for improved values of the K ratio. While the coupling of ionization is strongly related to the optical phonon modes, both dielectric and band gap contributions cannot be ignored. When applied as a candidate screen, the resulting model for K suggests design rules - simple structural restrictions - on scintillating radiation detector materials.

This paper presents new development methods for property-screening design rules, using structure-property relationships for two fundamental properties of activated scintillating based gamma radiation detection-luminosity and stopping power. The first and most evident goal in developing screening models of luminosity and stopping power, as indicated by the weight and electron densities, is to obtain new candidate cerium scintillating materials. However, a second and more strategic goal is to extract design rules, which define the structural limitations on materials consistent with desirable detector properties. These design rules are based on our capability to predict the luminescence and stopping power of a material from a set of structural descriptors. Predictive models are generated using statistical multiple linear regression over a set of 24 descriptors. We find that within a set of ten cerium-doped scintillator materials we can quantitatively predict luminosity with a correlation coefficient of ~0.94 based on 4 of the 24 descriptors, improving to ~0.99 with 6 descriptors; and electron density to ~0.99 with 3 descriptors. Furthermore, we show in this circumstance that the luminosity and stopping power are only nominally related. In particular, luminosity depends largely on matrix valence electron properties and their coupling to activator sites-properties that do not require high atomic masses or atomic numbers per se, requirements for high stopping power.