First-Principles Study of Amorphous Al2O3 ALD Coating in Li-S Battery Electrode Design

The Li-S battery is exceptionally appealing as an alternative candidate beyond Li-ion battery technology due to its promising high specific energy capacity. However, several obstacles (e.g., polysulfides’ dissolution, shuttle effect, high volume expansion of cathode, etc.) remain and thus hinder the commercialization of the Li-S battery. To overcome these challenges, a fundamental study based on atomistic simulation could be very useful. In this work, a comprehensive investigation of the adsorption of electrolyte (solvent and salt) molecules, lithium sulfide, and polysulfide (Li2Sx with 2 ≤x≤ 8) molecules on the amorphous Al2O3 atomic layer deposition (ALD) surface was performed using first-principles density functional theory (DFT) calculations. The DFT results indicate that the amorphous Al2O3 ALD surface is selective in chemical adsorption towards lithium sulfide and polysulfide molecules compared to electrolytes. Based on this work, it suggests that the Al2O3 ALD is a promising coating material for Li-S battery electrodes to mitigate the shuttling problem of soluble polysulfides.

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention Based Graph Neural Networks

Performing first principle calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use pre-defined features: based only on chemical compositions. We propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed method, which combines both atomic composition and atomic coordinates in 3D-space, improves the accuracy in voltage prediction by 17% when compared to composition based ML models. The first model directly learns the chemical reaction of electrodes and metal-ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (Eform) to compute their average voltage. Our models demonstrates improved accuracy in transferability from our subset of learned metal-ions to other metal-ions.

Considerations in Applying Neutron Depth Profiling (NDP) to Li-Ion Battery Research

Li distribution within micron-scale battery electrode materials is quantified with neutron depth profiling (NDP). This method allows the determination of intra- and inter-electrode parameters such as lithiation efficiency, electrode morphology...

A Model for Investigating Sources of Li-Ion Battery Electrode Heterogeneity:Part II. Active Material Size, Shape, Orientation, and Stiffness

This work is the extension of our previous paper [Nikpour et al., J. Electrochem. Soc. 168 060547, 2021] which introduced the multi-phase smoothed particle (MPSP) model. This model was used to simulate the evolution of the microstructure during the drying and calendering manufacturing processes of four different electrodes. The MPSP model uses particle properties to predict overall film properties such as conductivities and elastic moduli and is validated by multiple experiments. In this work the model is used to investigate the effects of active material particle size, shape, orientation, and stiffness on graphitic anodes. The model predicts that smaller active particles produce higher calendered film density, electronic conductivity, MacMullin number, and Young’s modulus, as compared to larger active particles. Rod-shaped active materials have greater ionic transport and lower electronic transport compared to the disk and sphere shapes, which have similar transport properties. During calendering, disk-shaped particles tend to be oriented horizontally, which decreases through-plane ionic transport. Increasing the stiffness of the active material increases film porosity and composite Young’s modulus, while lowering electronic transport and increasing ionic transport.

Viscosity Analysis of Battery Electrode Slurry

We report the effects of component ratios and mixing time on electrode slurry viscosity. Three component quantities were varied: active material (graphite), conductive material (carbon black), and polymer binder (carboxymethyl cellulose, CMC). The slurries demonstrated shear-thinning behavior, and suspension properties stabilized after a relatively short mixing duration. However, micrographs of the slurries suggested their internal structures did not stabilize after the same mixing time. Increasing the content of polymer binder CMC caused the greatest viscosity increase compared to that of carbon black and graphite.

Predicting synthesizability of crystalline materials via deep learning

Predicting the synthesizability of hypothetical crystals is challenging because of the wide range of parameters that govern materials synthesis. Yet, exploring the exponentially large space of novel crystals for any future application demands an accurate predictive capability for synthesis likelihood to avoid a haphazard trial-and-error. Typically, benchmarks of synthesizability are defined based on the energy of crystal structures. Here, we take an alternative approach to select features of synthesizability from the latent information embedded in crystalline materials. We represent the atomic structure of crystalline materials by three-dimensional pixel-wise images that are color-coded by their chemical attributes. The image representation of crystals enables the use of a convolutional encoder to learn the features of synthesizability hidden in structural and chemical arrangements of crystalline materials. Based on the presented model, we can accurately classify materials into synthesizable crystals versus crystal anomalies across a broad range of crystal structure types and chemical compositions. We illustrate the usefulness of the model by predicting the synthesizability of hypothetical crystals for battery electrode and thermoelectric applications.