Electrolyte Coatings for High Adhesion Interfaces in Solid-State Batteries from First Principles.

We introduce an adhesion parameter that enables rapid screening for materials interfaces with high adhesion. This parameter is obtained by density functional theory calculations of individual single-material slabs rather than slabs consisting of combinations of two materials, eliminating the need to calculate all configurations of a prohibitively vast space of possible interface configurations. Cleavage energy calculations are used as an upper bound for electrolyte and coating energies and implemented in an adapted contact angle equation to derive the adhesion parameter. In addition to good adhesion, we impose further constraints in electrochemical stability window, abundance, bulk reactivity, and stability to screen for coating materials for next-generation solid-state batteries. Good adhesion is critical in combating delamination and resistance to lithium diffusivity in solid-state batteries. Here, we identify several promising coating candidates for the Li7La3Zr2O12 and sulfide electrolyte systems including the previously investigated electrode coating materials LiAlSiO4 and Li5AlO8, making them especially attractive for experimental optimization and commercialization.

Temperature Extrapolation of Molecular Dynamics Simulations of Complex Chemistry to Microsecond Timescales Using Kinetic Models: Applications to Hydrocarbon Pyrolysis.

We develop a method to construct temperature-dependent kinetic models of hydrocarbon pyrolysis, based on information from molecular dynamics (MD) simulations of pyrolyzing systems in the high-temperature regime. MD simulations are currently a key tool to understand the mechanism of complex chemical processes such as pyrolysis and to observe their outcomes in different conditions, but these simulations are computationally expensive and typically limited to nanoseconds of simulation time. This limitation is inconsequential at high temperatures, where equilibrium is reached quickly, but at low temperatures, the system may not equilibrate within a tractable simulation timescale. In this work, we develop a method to construct kinetic models of hydrocarbon pyrolysis using the information from the high-temperature high-reactivity regime. We then extrapolate this model to low temperatures, which enables microsecond-long simulations to be performed. We show that this approach accurately predicts the time evolution of small molecules, as well as the size and composition of long carbon chains across a wide range of temperatures and compositions. Further, we show that the range of suitable temperatures for extrapolation can easily be improved by adding more simulations to the training data. Compared to experimental results, our kinetic model leads to similar compositional trends while allowing for more detailed kinetic and mechanistic insights.

Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium-Boron-Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes.

We report a solid-state Li-ion electrolyte predicted to exhibit simultaneously fast ionic conductivity, wide electrochemical stability, low cost, and low mass density. We report exceptional density functional theory (DFT)-based room-temperature single-crystal ionic conductivity values for two phases within the crystalline lithium-boron-sulfur (Li-B-S) system: 62 (+9, -2) mS cm-1 in Li5B7S13 and 80 (-56, -41) mS cm-1 in Li9B19S33. We report significant ionic conductivity values for two additional phases: between 0.0056 and 0.16 mS/cm -1 in Li2B2S5 and between 0.0031 and 9.7 mS cm-1 in Li3BS3 depending on the room-temperature extrapolation scheme used. To our knowledge, our prediction gives Li9B19S33 and Li5B7S13 the second and third highest reported DFT-computed single-crystal ionic conductivities of any crystalline material. We compute the thermodynamic electrochemical stability window widths of these materials to be 0.50 V for Li5B7S13, 0.16 V for Li2B2S5, 0.45 V for Li3BS3, and 0.60 V for Li9B19S33. Individually, these materials exhibit similar or better ionic conductivity and electrochemical stability than the best-known sulfide-based solid-state Li-ion electrolyte materials, including Li10GeP2S12 (LGPS). However, we predict that electrolyte materials synthesized from a range of compositions in the Li-B-S system may exhibit even wider thermodynamic electrochemical stability windows of 0.63 V and possibly as high as 3 V or greater. The Li-B-S system also has a low elemental cost of approximately 0.05 USD/m2 per 10 µm thickness, which is significantly lower than that of germanium-containing LGPS, and a comparable mass density below 2 g/cm3. These fast-conducting phases were initially brought to our attention by a machine learning-based approach to screen over 12,000 solid electrolyte candidates, and the evidence provided here represents an inspiring success for this model.

Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data.

Machine learning (ML) methods have the potential to revolutionize materials design, due to their ability to screen materials efficiently. Unlike other popular applications such as image recognition or language processing, large volumes of data are not available for materials design applications. Here, we first show that a standard learning approach using generic descriptors does not work for small data, unless it is guided by insights from physical equations. We then propose a novel method for transferring such physical insights onto more generic descriptors, allowing us to screen billions of unknown compositions for Li-ion conductivity, a scale which was previously unfeasible. This is accomplished by using the accurate model trained with physical insights to create a large database, on which we train a new ML model using the generic descriptors. Unlike previous applications of ML, this approach allows us to screen materials which have not necessarily been tested before (i.e., not on ICSD or Materials Project). Our method can be applied to any materials design application where a small amount of data is available, combined with high details of physical understanding.

Density functional theory calculations for evaluation of phosphorene as a potential anode material for magnesium batteries.

We have systematically investigated black phosphorus and its derivative - a novel 2D nanomaterial, phosphorene - as an anode material for magnesium-ion batteries. We first performed Density Functional Theory (DFT) simulations to calculate the Mg adsorption energy, specific capacity, and diffusion barriers on monolayer phosphorene. Using these results, we evaluated the main trends in binding energy and voltage as a function of Mg concentration. Our studies revealed the following findings: (1) Mg bonds strongly with the phosphorus atoms and exists in the cationic state; (2) Mg diffusion on phosphorene is fast and anisotropic with an energy barrier of only 0.09 eV along the zigzag direction; (3) the theoretical specific capacity is 865 mA h g-1 with an average voltage of 0.833 V (vs. Mg/Mg2+), ideal for use as an anode. Given these results, we conclude that phosphorene is a very promising anode material for Mg-ion batteries. We then expand our simulations to the case of bulk black phosphorus, where we again find favorable binding energies. We also find that bulk black phosphorous must overcome a structural stress of 0.062 eV per atom due to a volumetric expansion of 33% during magnesiation. We found that the decrease in particle size is good to increase its specific capacity.

Atomic Layer Deposition of Stable LiAlF4 Lithium Ion Conductive Interfacial Layer for Stable Cathode Cycling.

Modern lithium ion batteries are often desired to operate at a wide electrochemical window to maximize energy densities. While pushing the limit of cutoff potentials allows batteries to provide greater energy densities with enhanced specific capacities and higher voltage outputs, it raises key challenges with thermodynamic and kinetic stability in the battery. This is especially true for layered lithium transition-metal oxides, where capacities can improve but stabilities are compromised as wider electrochemical windows are applied. To overcome the above-mentioned challenges, we used atomic layer deposition to develop a LiAlF4 solid thin film with robust stability and satisfactory ion conductivity, which is superior to commonly used LiF and AlF3. With a predicted stable electrochemical window of approximately 2.0 ± 0.9 to 5.7 ± 0.7 V vs Li+/Li for LiAlF4, excellent stability was achieved for high Ni content LiNi0.8Mn0.1Co0.1O2 electrodes with LiAlF4 interfacial layer at a wide electrochemical window of 2.75-4.50 V vs Li+/Li.

Data Mining for New Two- and One-Dimensional Weakly Bonded Solids and Lattice-Commensurate Heterostructures.

Layered materials held together by weak interactions including van der Waals forces, such as graphite, have attracted interest for both technological applications and fundamental physics in their layered form and as an isolated single-layer. Only a few dozen single-layer van der Waals solids have been subject to considerable research focus, although there are likely to be many more that could have superior properties. To identify a broad spectrum of layered materials, we present a novel data mining algorithm that determines the dimensionality of weakly bonded subcomponents based on the atomic positions of bulk, three-dimensional crystal structures. By applying this algorithm to the Materials Project database of over 50,000 inorganic crystals, we identify 1173 two-dimensional layered materials and 487 materials that consist of weakly bonded one-dimensional molecular chains. This is an order of magnitude increase in the number of identified materials with most materials not known as two- or one-dimensional materials. Moreover, we discover 98 weakly bonded heterostructures of two-dimensional and one-dimensional subcomponents that are found within bulk materials, opening new possibilities for much-studied assembly of van der Waals heterostructures. Chemical families of materials, band gaps, and point groups for the materials identified in this work are presented. Point group and piezoelectricity in layered materials are also evaluated in single-layer forms. Three hundred and twenty-five of these materials are expected to have piezoelectric monolayers with a variety of forms of the piezoelectric tensor. This work significantly extends the scope of potential low-dimensional weakly bonded solids to be investigated.

Simulated cytoskeletal collapse via tau degradation.

We present a coarse-grained two dimensional mechanical model for the microtubule-tau bundles in neuronal axons in which we remove taus, as can happen in various neurodegenerative conditions such as Alzheimers disease, tauopathies, and chronic traumatic encephalopathy. Our simplified model includes (i) taus modeled as entropic springs between microtubules, (ii) removal of taus from the bundles due to phosphorylation, and (iii) a possible depletion force between microtubules due to these dissociated phosphorylated taus. We equilibrate upon tau removal using steepest descent relaxation. In the absence of the depletion force, the transverse rigidity to radial compression of the bundles falls to zero at about 60% tau occupancy, in agreement with standard percolation theory results. However, with the attractive depletion force, spring removal leads to a first order collapse of the bundles over a wide range of tau occupancies for physiologically realizable conditions. While our simplest calculations assume a constant concentration of microtubule intercalants to mediate the depletion force, including a dependence that is linear in the detached taus yields the same collapse. Applying percolation theory to removal of taus at microtubule tips, which are likely to be the protective sites against dynamic instability, we argue that the microtubule instability can only obtain at low tau occupancy, from 0.06-0.30 depending upon the tau coordination at the microtubule tips. Hence, the collapse we discover is likely to be more robust over a wide range of tau occupancies than the dynamic instability. We suggest in vitro tests of our predicted collapse.