Predicting Reactivity and Passivation of Solid-State Battery Interfaces.

In this work, we build a computationally inexpensive, data-driven model that utilizes atomistic structure information to predict the reactivity of interfaces between any candidate solid-state electrolyte material and a Li metal anode. This model is trained on data from ab initio molecular dynamics (AIMD) simulations of the time evolution of the solid electrolyte-Li metal interfaces for 67 different materials. Predicting the reactivity of solid-state interfaces with ab initio techniques remains an elusive challenge in materials discovery and informatics, and previous work on predicting interfacial compatibility of solid-state Li-ion electrolytes and Li metal anodes has focused mainly on thermodynamic convex hull calculations. Our framework involves training machine learning models on AIMD data, thereby capturing information on both kinetics and thermodynamics, and then leveraging these models to predict the reactivity of thousands of new candidates in the span of seconds, avoiding the need for additional weeks-long AIMD simulations. We identify over 300 new chemically stable and over 780 passivating solid electrolytes that are predicted to be thermodynamically unfavored. Our results indicate many potential solid-state electrolyte candidates have been incorrectly labeled unstable via purely thermodynamic approaches using density functional theory (DFT) energetics, and that the pool of promising, Li-stable solid-state electrolyte materials may be much larger than previously thought from screening efforts. To showcase the value of our approach, we highlight two borate materials that were identified by our model and confirmed by further AIMD calculations to likely be highly conductive and chemically stable with Li: LiB13C2 and LiB12PC.

Reactive Vapor-Phase Inhibitors for Area-Selective Depositions at Tunable Critical Dimensions.

Area-selective depositions (ASD) take advantage of the chemical contrast between material surfaces in device fabrication, where a film can be selectively grown by chemical vapor deposition on metal versus a dielectric, for instance, and can provide a path to nontraditional device architectures as well as the potential to improve existing device fabrication schemes. While ASD can be accessed through a variety of methods, the incorporation of reactive moieties in inhibitors presents several advantages, such as increasing thermal stability and limiting precursor diffusion into the blocking layer. Alkyne-terminated small molecule inhibitors (SMIs)─propargyl, dipropargyl, and tripropargylamine─were evaluated as metal-selective inhibitors. Modeling these SMIs provided insight into the binding mechanism, influence of sterics, and complex polymer network formed from the reaction between inhibitors consisting of alkene, aromatic, and network branchpoints. While a significant contrast in the binding of the SMIs on copper versus a dielectric was observed, residual amounts were detected on the dielectric surfaces, leading to variable ALD growth rates dependent on pattern-critical dimensions. This behavior can be controlled and utilized to direct film growth on patterns only above a critical threshold dimension; below this threshold, both the dielectric and metal features are protected. This method provides another design parameter for ASD processes and may extend its application to broader-ranging device fabrication schemes.

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.