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DOE OSTI2024

Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2

The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.

25 ENERGY STORAGE↗

DOE OSTI2022

Understanding the Solid-State Electrode–Electrolyte Interface of a Model System Using First-Principles Statistical Mechanics and Thin-Film X-ray Characterization

Intermixing of atomic species at the electrode-electrolyte boundaries can impact the properties of the interfaces in solid-state batteries. Herein, this work uses first-principles statistical mechanics along with experimental characterization to understand intermixing at the electrode-electrolyte interface. For the model presented in this work, lithium manganese oxide (LiMn 2 O 4 , LMO) and lithium lanthanum titanate (Li 3x La 2/3-x TiO 3 , LLTO) are employed as the cathode and electrolyte, respectively. The results of the computational work show that Ti-Mn intermixing at the interface is significant at synthesis temperatures. The experimental results in this work find that, at some critical temperatures between 600 and 700 degrees C for material preparation, the interface of LLTO-LMO becomes blurred. Calculations predict that the interface is unstable with regard to Ti-Mn intermixing starting at 0 K, suggesting that the critical temperature found in the experiment is related to kinetics. The work overall suggests that, in designing a solid-state battery, the fundamental reactions such as intermixing need to be considered.

25 ENERGY STORAGE↗

DOE OSTI2020

Toward Higher Voltage Solid-State Batteries by Metastability and Kinetic Stability Design

The energy density of battery systems is limited largely by the electrochemical window of the electrolyte. Here, the combined thermodynamic and kinetic effects of mechanically induced metastability are shown to greatly widen the operational voltage window of solid-state batteries based on ceramic-sulfide electrolytes. Solid electrolyte voltage stability up to 10 V is achieved with minimal degradation, far beyond the capability of organic liquid electrolytes. Furthermore, combined experiment, ab initio computation, and theoretical modeling identify the nature of mechanically constrained Li 10 GeP 2 S 12 decomposition both within the bulk and at interfaces with cathode materials at very high voltages. Previously unclear kinetic processes are identified that, when properly implemented, can potentially allow solid-state full cells with remarkably high operational voltages.

25 ENERGY STORAGE↗

DOE OSTI2026

Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation

The performance of rechargeable batteries is fundamentally influenced by the physicochemical properties and microstructural features of their key material components. Recent experimental advancements have highlighted the potential of single-crystal (SC) morphologies to address inherent limitations of polycrystalline (PC) electrodes and solid-state electrolytes, offering tunable charge transport kinetics and improved cell cycling performance. Here, this review examines how state-of-the-art computational modeling, from atomistic and mesoscale to continuum-level approaches, including machine learning methodologies, has been utilized to investigate the critical factors governing the electrochemical behavior of SC battery materials. We explore how predictive modeling can elucidate the processing–structure–property–performance relationships of SC cathodes, anodes, and solid-state electrolytes, with a focus on unique SC characteristics such as crystallographic anisotropy, size effects, and facet-dependent properties. Additionally, we identify limitations in commonly used modeling techniques and discuss strategies to address these challenges. By integrating high-fidelity simulations with experimental insights, this review aims to outline a clear path for the rational design and optimization of SC battery components, paving the way for accelerated advancements in energy storage technologies.

Materials science↗

DOE OSTI2022

Modeling assisted synthesis of Zr-doped Li 3-x In 1-x Zr x Cl 6 with ultrahigh ionic conductivity for lithium-ion batteries

All-solid-state lithium-ion batteries (ASSLBs) are an important milestone for the future of energy storage because of their capability of impressive energy density and outstanding safety. However, oxide and sulfide solid-state electrolytes (SSEs) suffer from either low ionic conductivity or poor chemical stability. In contrast, halide-based SSEs, are promising as candidate materials owing to high conductivity, good stability, and broad cathode compatibility. Though element doping of the SSEs is an effective and common approach to further improve their electrochemical properties, dopant exploration and optimization through solely experimental trials are both costly and time-consuming. For this aspect, computational simulations for dopant element and concentration screening are adopted in this research and zirconium is selected as a suitable dopant for Li 3 InCl 6 . Further, the synthesized Li 2.75 In 0.75 Zr 0.25 Cl 6 exhibited Li ionic conductivity of 5.82 x 10 -3 Scm -1 at room temperature, which is the highest among reported halide SSEs. The ASSLB formed with Li 2 CoO 2 -Li 2.75 In 0.75 Zr 0.25 Cl 6 -Li/In delivers a high initial capacity of 129.3 mAh∙g -1 . Conclusively, this work provides an effective approach which combines computational modeling and experimental verification for the development of halide SSEs with improved stability and conductivity. The successful design approach and compelling results provide further possibilities and capabilities in future SSE research.

25 ENERGY STORAGE↗

DOE OSTI2023

Two-layer cathode architecture for high-energy density and high-power density solid state batteries

Solid state batteries with high-energy density and high-power density require the development of thick and energy dense cathodes. Structured cathode electrodes with a double-layer configuration were enabled using a freeze tape casting technique. A bottom dense layer was utilized to enhance the energy density whereas a top porous layer with vertically aligned walls was utilized to enhance the power density. The porous structure of the power layer was achieved by ice templating this layer on top of the densified energy layer of the cathode. This configuration was found to better utilize the active material of the cathodes and was optimized using numerical simulation and computer modeling. Cells with Li metal anode and LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) at approximately 5 and 20 mg/cm 2 were cycled at 70 °C at different C-rates. Poly(ethylene oxide) (PEO) with lithium bis-trifluoromethanesulfonimide (LiTFSI) was used for the catholyte and the solid-state electrolyte. The structured cathodes exhibited more than double capacity values as well as better Coulombic efficiency compared to non-structured (single-layer) thick cathodes. In conclusion, synchrotron X-ray tomography and scanning electron microscopy were used to characterize the microstructure of the cathodes.

25 ENERGY STORAGE↗

DOE OSTI2025

Atomic layer deposited aluminum nitride for interface protection of lithium germanium thiophosphate solid electrolytes

Lithium germanium thiophosphate (LGPS) is an attractive solid-state electrolyte material due to its exceptionally high ionic conductivity (~1.2 × 10 –2 S cm –1 ), comparable to many organic liquid electrolytes commonly used in batteries. Despite the high conductivity of LGPS, the susceptibility of LGPS to deleterious degradation reactions has impeded its commercial adoption into solid-state batteries. In particular, the poor voltage stability of LGPS with high-voltage cathode or lithium metal potentials often results in dramatically increasing cell impedance during galvanic cycling. Here, we use aluminum nitride (AlN) as a protection layer for Li metal anode, applied directly to the LGPS at 250 °C using plasma-enhanced atomic layer deposition, to enhance cell performance by preventing LGPS-Li metal reactions. We compare the surface chemistry and electrochemical cycling performance of atomic layer-deposited AlN grown using both plasma N 2 and NH 3 precursors. Galvanostatic cycling and electrochemical impedance spectroscopy show that AlN-coated LGPS cells perform better than bare LGPS cells in contact with Li metal anodes, with the AlN able to improve cycling longevity by over a factor of 3 in certain cases. Finally, we utilize x-ray photoelectron spectroscopy (XPS) line scans to highlight the slow room-temperature reactivity between AlN and evaporated lithium metal, and a computational model is built to aid further XPS analysis.

25 ENERGY STORAGE↗

DOE OSTI2025

Electrolytic gold plating, stripping, and ion transport dynamics through a solid-state iodide perovskite

The pronounced electrochemical reactivity between halide perovskites and metal electrodes can introduce mobile extrinsic metal ions which can cause device instability or enable novel functionalities. Here we systematically investigate the kinetics of gold cation (Au + ) migration in indium tin oxide (ITO)/methylammonium lead triiodide (MAPbI 3 )/Au model devices under long-term potentiostatic biasing. Scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and density functional theory (DFT) analyses reveal that Au + ions, electrochemically generated at the Au anode, traverse the perovskite layer with diffusion coefficients on the order of 10 −11 to 10 −10 cm 2 s −1 and are subsequently reduced at the cathode as Au 0 clusters, resembling metal plating behavior in electrolytic cells and solid-state batteries during charging. Furthermore, reversing the applied bias strips the plated Au 0 , revealing reversibility suitable for bipolar resistive switching devices and providing direct evidence of the electrochemical and ionic nature of Au transport within the perovskite matrix. Quantitatively determining diffusion coefficients and ion concentrations provides foundational inputs for future drift-diffusion modelling opportunities and allows us to relate our findings to implications on long term operation of devices like photovoltaic modules. These results clearly demonstrate the solid-state electrochemical nature of perovskite devices, highlight methods to be more quantitative about ion transport properties, provide and emphasize the importance of disentangling electro-, photo-, photoelectrochemical processes for understanding device performance and unlocking new functionalities.

14 SOLAR ENERGY↗