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Wan, Liwen F.

Publications and source records attributed to Wan, Liwen F..

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Probing degradation at solid-state battery interfaces using machine-learning interatomic potential

Solid-state batteries featuring fast ion-conducting solid electrolytes are promising next-generation energy storage technologies, yet challenges remain for practical deployment due to electro-chemo-mechanical instabilities at solid-solid interfaces. These interfaces, which include homogeneous/internal interfaces such as grain boundaries (GBs) and heterogeneous/external interfaces between solid-electrolyte and electrode materials, can impede Li-ion transport, deteriorate performance, and eventually lead to cell failure. Here, in this study, we leverage large-scale molecular simulations, enabled by validated machine-learning interatomic potentials, to directly probe the onset of interfacial degradation at the garnet Li 7 La 3 Zr 2 O 12 (LLZO) solid-electrolyte/LiCoO 2 (LCO) cathode interface. By surveying different interfacial geometries and compositions, it is found that Li-deficient interfaces can lead to severe interfacial disordering with cation mixing and Co interdiffusion from LCO into LLZO. By contrast, Li-sufficient interfaces are less disordered, although elemental segregation with local ordering is observed. As a consequence of Co interdiffusion, Co-rich regions are formed at the GBs of LLZO due to cation segregation and trapping effects. This behavior is independent of the GB tilting axis, degree of disorder at the GBs, and Co concentration, which implies Co clustering at GBs is a general phenomenon in polycrystalline LLZO and can dictate its overall transport and mechanical properties. Our findings elucidate the underlying fundamental mechanisms that give rise to experimentally observed physicochemical properties and provide guidelines for interface design that can mitigate interfacial degradation and improve cycling performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Assessing cathode–electrolyte interphases in batteries

The cathode-electrolyte interphase plays a pivotal role in determining the usable capacity and cycling stability of electrochemical cells, yet it is overshadowed by its counterpart, the solid-electrolyte interphase. This is primarily due to the prevalence of side reactions, particularly at low potentials on the negative electrode, especially in state-of-the-art Li-ion batteries where the charge cutoff voltage is limited. However, as the quest for high-energy battery technologies intensifies, there is a pressing need to advance the study of cathode-electrolyte interphase properties. Here, we present a comprehensive approach to analyse the cathode-electrolyte interphase in battery systems. We underscore the importance of employing model cathode materials and coin cell protocols to establish baseline performance. Additionally, we delve into the factors behind the inconsistent and occasionally controversial findings related to the cathode-electrolyte interphase. In conclusion, we also address the challenges and opportunities in characterizing and simulating the cathode-electrolyte interphase, offering potential solutions to enhance its relevance to real-world applications.

25 ENERGY STORAGE↗
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 OSTI2024

Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study

To improve the performance of electrochemical devices, it is essential to understand the effects of nonequilibrium motifs in solids, such as grain boundaries, amorphous phases, and highly strained regions, on atomic-scale transport and stability. Molecular dynamics simulations are used to explore the combined effect of far-from-equilibrium atomic structures and the choice of interatomic potential on ionic diffusivity predictions for Li 7 La 3 Zr 2 O 12 (LLZO), a promising solid electrolyte for all-solid-state batteries. Amorphization and high strain are considered using both classical Buckingham interatomic potentials and machine learning force fields. Here we find that both crystalline expansion and amorphization tend to slow diffusion, although the different physical encodings in the two potentials impact the properties in different ways. We trace these variations to a combination of structural and transport factors, the contributions of which are deconvoluted computationally. Graph-based analysis reveals that the variations for amorphous LLZO arise from the connectivity of diffusion pathways within the predicted structures, which generally correlates with diffusivity and is notably higher for structures generated by the machine learning force fields. Our study provides additional insight into the relationship between atomic structure and diffusivity in LLZO, while also highlighting the need for care in choosing and validating potentials to simulate far from equilibrium structures.

25 ENERGY STORAGE↗
DOE OSTI2024

Integrating Machine Learning Potential and X-ray Absorption Spectroscopy for Predicting the Chemical Speciation of Disordered Carbon Nitrides

Precise determination of atomic structural information in functional materials holds transformative potential and broad implications for emerging technologies. Spectroscopic techniques, such as X-ray absorption near-edge structure (XANES), have been widely used for material characterization; however, extracting chemical information from experimental probes remains a significant challenge, particularly for disordered materials. We present an integrated approach that combines atomic simulations, data-driven techniques, and experimental measurements to investigate chemical speciation of amorphous carbon nitride systems as a case study. Here, we discuss the development of machine learning potentials that can efficiently explore the vast configuration space of amorphous carbon nitrides. By employing statistical methods, this structural database enables the elucidation of the most representative local structures and how they evolve with chemical compositions and density. Density functional theory simulations are used to establish a correlation between the local structure and spectroscopic signatures, which then serve as the basis for interpreting and extracting chemical content from experimental data. Although our framework is specifically demonstrated for XANES and carbon nitrides, the approach described herein is readily adaptable as applied to other experimental characterization probes and materials classes.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Coupled Experimental–Theoretical Characterization of a Carbon Electrode in Vanadium Redox Flow Batteries using X-ray Absorption Spectroscopy

Vanadium redox flow batteries (VRFBs) have emerged as promising solutions for stationary grid energy storage due to their high efficiency, scalability, safety, near room-temperature operation conditions, and the ability to independently size power and energy capacities. The performance of VRFBs heavily relies on the redox couple reactions of V 2+ /V 3+ and VO 2+ /VO 2 + on carbon electrodes. Therefore, a thorough understanding of the surface functionality of carbon electrodes and their propensity for degradation during electrochemical cycles is crucial for designing VRFBs with extended lifespans. In this study, we present a coupled experimental–theoretical approach based on carbon K edge X-ray absorption spectroscopy (XAS) to characterize carbon electrodes prepared under different conditions and identify relevant functional groups that contribute to unique spectroscopic features. Atomic models were created to represent functional groups, such as hydroxyl, carboxyl, methyl, and aldehyde, bonded to carbon atoms in either sp 2 or sp 3 environments. The interactions between functionalized carbon and various solvated vanadium complexes were modeled using density functional theory. A library of carbon K-edge XAS spectra was generated for distinct carbon atoms in different functional groups, both before and after interacting with solvated vanadium complexes. Here we demonstrate how these simulated spectra can be used to deconvolve ex situ experimental spectra measured from carbon electrodes and to track changes in the electrode composition following immersion in different electrolytes or extended cycling within a functional VRFB. By doing so, we identify the active species present on the carbon electrodes, which play a crucial role in determining their electrochemical performance.

25 ENERGY STORAGE↗
DOE OSTI2024

Understanding electronic structure tunability by metal dopants for promoting MgB 2 hydrogenation

Hydrogen is a promising energy carrier, but its onboard application is limited by the need for compact, low-pressure storage solutions. Solid-state complex metal hydride systems, such as MgB 2 /Mg(BH 4 ) 2 , offer high storage capacities but suffer from sluggish kinetics and poor reversibility. One avenue for improving reactivity is to introduce metal dopants to alter electronic and atomic properties, but the role of these chemical additives remains poorly understood, particularly for the hydrogenation reaction. In this work, we used density functional theory calculations on model MgB 2 systems to rationalize the potential role of metal dopants in destabilizing B–B bonding within the MgB 2 lattice. We carried out detailed electronic structure analyses for 28 different metal dopant adatoms to identify properties that contribute to a dopant’s efficacy. Based on the simulation results, we propose that an intermediate ionic and covalent character of the bonds between adatoms and B atoms is desirable for facilitating charge redistribution, disrupting the B–B bond network, and promoting H 2 dissociation and H atom chemisorption on MgB 2 .

08 HYDROGEN↗
DOE OSTI2023

Harnessing Neural Networks for Elucidating X-ray Absorption Structure–Spectrum Relationships in Amorphous Carbon

Improved understanding of structural and chemical properties through local experimental probes, such as X-ray absorption near-edge structure (XANES) spectroscopy, is crucial for the understanding and design of functional materials. In recent years, significant advancements have been made in the development of data science approaches for the automated interpretation of XANES structure–spectrum relationships. However, existing studies have primarily focused on crystalline solids and small molecules, while fewer efforts have been devoted to disordered systems. Thus, in this work, we demonstrate the development of neural network models for predicting and interpreting XANES spectra of amorphous carbon (a-C) from local structural descriptors. Comparison between different structural descriptors expectedly shows that the inclusion of both bond length and bond angle information is necessary for an accurate prediction of the spectra. Among the descriptors considered in this work, we find that the local many-body tensor representation yields the highest accuracy and greatest interpretability so that it can be leveraged to understand the importance of structural motifs in determining XANES spectra. Furthermore, we also discuss performance of neural network models for predicting both local structure features, such as bond lengths and bond angles, and global chemical composition, such as the sp:sp 2 :sp 3 ratio.

36 MATERIALS SCIENCE↗
DOE OSTI2023

The Riddle of Dark LLZO: Cobalt Diffusion in Garnet Separators of Solid‐State Lithium Batteries

Abstract Solid‐state batteries (SSBs) with a Li 7 La 3 Zr 2 O 12 (LLZO) garnet electrolyte are attracting much attention as robust and safe alternative to conventional lithium‐ion batteries. Technical challenges in the practical implementation of garnet SSBs are related to the need for high‐temperature sintering, which often leads to undesirable chemical reactions with the cathode material. While these reactions are well understood for composite cathodes, very little is known about similar processes between cathode and separator during battery fabrication. This work focuses on understanding the processes between the composite LiCoO 2 ‐LLZO cathode and the LLZO separator and how they affect the battery performance. The extensive diffusion of Co‐ions within LLZO, which leads to the often‐observed LLZO darkening, is shown to have a significant impact on ionic conductivity, electronic conductivity, and dendrite stability of the separator. Experimental data coupled with large‐scale molecular dynamics simulations uncover the diffusion mechanism for Co‐ions and identify secondary phases that form during these interactions. In addition to extensive Co‐ion diffusion within the grains, a non‐uniform segregation of Co‐ions at grain boundaries is found leading to the formation of three distinct Co‐containing phases. This work offers a general approach to studying the fundamental ion diffusion processes that occur during the fabrication of oxide SSBs.

electrochemical properties↗
DOE OSTI2023

Influence of External Conditions on the Black-to-Yellow Phase Transition of CsPbI 3 Based on First-Principles Calculations: Pressure and Moisture

All-inorganic CsPbI 3 perovskite has emerged as a promising candidate for next-generation solar cells. However, owing to the poor structural stability of black phase CsPbI 3 perovskite at room temperature, it spontaneously transforms to the yellow, photoinactive, nonperovskite phase at room temperature, limiting further development of CsPbI 3 perovskite solar cells. Here, to better understand the mechanism driving such undesirable phase transformation, we examine the thermodynamics and kinetics associated with γ-to-δ phase transformation using density functional theory calculation. A solid-state nudged elastic band method was employed to find minimum energy pathways assuming a concerted solid-state phase transformation between these two phases. The gas molecules representing atmospheric (H 2 O, O 2 , and N 2 ) and inert (Ar) ambient environments, as well as applied external pressure, were considered to examine the influence of external conditions on the black-to-yellow phase transition. Our calculation reveals that, with increasing pressure, the thermodynamic driving force for converting the γ-phase to the δ-phase increases, whereas a larger activation energy must be overcome for the phase transition to occur. We also investigate the moisture-induced phase transformation with an H 2 O molecule and its dissociated species (H + /OH – ) and demonstrate that the reaction energy barriers can be significantly lowered in the presence of H 2 O or OH – . On the other hand, other nonpolar species (O 2 , N 2 , and Ar) have a negligible effect on the phase transformation kinetics, while they might reduce the thermodynamic driving force for the phase transformation and suppress the undesirable phase transformation. Our theoretical prediction could support recent observations that the γ-to-δ phase transformation occurs rapidly and catalytically in the presence of moisture, whereas in dry argon and dry oxygen atmospheres, the γ-CsPbI 3 remains stable.

14 SOLAR ENERGY↗
DOE OSTI2022

Oxide‐Based Solid‐State Batteries: A Perspective on Composite Cathode Architecture

Abstract The garnet‐type phase Li 7 La 3 Zr 2 O 12 (LLZO) attracts significant attention as an oxide solid electrolyte to enable safe and robust solid‐state batteries (SSBs) with potentially high energy density. However, while significant progress has been made in demonstrating compatibility with Li metal, integrating LLZO into composite cathodes remains a challenge. The current perspective focuses on the critical issues that need to be addressed to achieve the ultimate goal of an all‐solid‐state LLZO‐based battery that delivers safety, durability, and pack‐level performance characteristics that are unobtainable with state‐of‐the‐art Li‐ion batteries. This perspective complements existing reviews of solid/solid interfaces with more emphasis on understanding numerous homo‐ and heteroionic interfaces in a pure oxide‐based SSB and the various phenomena that accompany the evolution of the chemical, electrochemical, structural, morphological, and mechanical properties of those interfaces during processing and operation. Finally, the insights gained from a comprehensive literature survey of LLZO–cathode interfaces are used to guide efforts for the development of LLZO‐based SSBs.

25 ENERGY STORAGE↗
DOE OSTI2022

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li 7 La 3 Zr 2 O 12 solid electrolytes

Batteries based on solid-state electrolytes, including Li 7 La 3 Zr 2 O 12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. Here these examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

25 ENERGY STORAGE↗
DOE OSTI2022

Understanding Hydrogenation Chemistry at MgB 2 Reactive Edges from Ab Initio Molecular Dynamics

Solid-state hydrogen storage materials often operate via transient, multistep chemical reactions at complex interfaces that are difficult to capture. Here, we use direct ab initio molecular dynamics simulations at accelerated temperatures and hydrogen pressures to probe the hydrogenation chemistry of the candidate material MgB 2 without a priori assumption of reaction pathways. Focusing on highly reactive ($10\overline{1}0$) edge planes where initial hydrogen attack is likely to occur, we track mechanistic steps toward the formation of hydrogen-saturated BH 4 – units and key chemical intermediates, involving H 2 dissociation, generation of functionalities and molecular complexes containing BH 2 and BH 3 motifs, and B–B bond breaking. Further, the genesis of higher-order boron clustering is also observed. Different charge states and chemical environments at the B-rich and Mg-rich edge planes are found to produce different chemical pathways and preferred speciation, with implications for overall hydrogenation kinetics. The reaction processes rely on B–H bond polarization and fluctuations between ionic and covalent character, which are critically enabled by the presence of Mg 2+ cations in the nearby interphase region. Our results provide guidance for devising kinetic improvement strategies for MgB 2 -based hydrogen storage materials, while also providing a template for exploring chemical pathways in other solid-state energy storage reactions.

08 HYDROGEN↗
DOE OSTI2022

First-Principles Elucidation of Initial Dehydrogenation Pathways in Mg(BH 4 ) 2

Complex borohydrides such as Mg(BH 4 ) 2 offer one of highest capacities to chemically store hydrogen for onboard applications; however, it suffers greatly from kinetic constraints that prevent realization of full capacity and reversibility. Understanding these kinetic limitations solely from experiments is extremely challenging due to the unusual complexity of various competing elemental reaction steps involved during the de/rehydrogenation reaction. This work aims to map out the energetics associated with initial dehydrogenation of Mg(BH 4 ) 2 from first-principles simulations and to identify the preferred reaction pathways. Our calculations suggest the rate-limiting step during BH 4 – –B 3 H 8 – conversion is the formation of the B 2 H 7 – intermediate. We further emphasize and clarify that the B 3 H 8 – and H – intermediates, formed during initial Mg(BH 4 ) 2 decomposition, appear as molecular species that are embedded in the Mg–BH 4 –Mg matrix as evidenced in the nuclear magnetic resonance measurements and not as bulk MgH 2 and Mg(B 3 H 8 ) 2 as previously assumed in theoretical predictions of the thermodynamics.

08 HYDROGEN↗
DOE OSTI2021

Microstructural impacts on ionic conductivity of oxide solid electrolytes from a combined atomistic-mesoscale approach

Abstract Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged, practical processing and synthesis routes introduce grain boundaries and other interfaces that can perturb primary conduction channels. To directly probe these effects, we demonstrate an efficient and general mesoscopic computational method capable of predicting effective ionic conductivity through a complex polycrystalline oxide-based solid electrolyte microstructure without relying on simplified equivalent circuit description. We parameterize the framework for Li 7- x La 3 Zr 2 O 12 (LLZO) garnet solid electrolyte by combining synthetic microstructures from phase-field simulations with diffusivities from molecular dynamics simulations of ordered and disordered systems. Systematically designed simulations reveal an interdependence between atomistic and mesoscopic microstructural impacts on the effective ionic conductivity of polycrystalline LLZO, quantified by newly defined metrics that characterize the complex ionic transport mechanism. Our results provide fundamental understanding of the physical origins of the reported variability in ionic conductivities based on an extensive analysis of literature data, while simultaneously outlining practical design guidance for achieving desired ionic transport properties based on conditions for which sensitivity to microstructural features is highest. Additional implications of our results are discussed, including a possible connection between ion conduction behavior and dendrite formation.

25 ENERGY STORAGE↗