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Adelstein, Nicole

Publications and source records attributed to Adelstein, Nicole.

14 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

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 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 OSTI2021

Impacts of vacancy-induced polarization and distortion on diffusion in Solid Electrolyte Li 3 OCl

We report that Lithium-rich oxychloride antiperovskites are promising solid electrolytes for enabling next-generation batteries. Here, we report a comprehensive study varying Li + concentrations in Li 3 OCl using ab initio molecular dynamics simulations. The simulations accurately capture the complex interactions between Li + vacancies (V$^'_{Li}$), the dominant mobile species in Li 3 OCl. The V$^'_{Li}$ polarize and distort the host lattice, inducing additional non-vacancy-mediated diffusion mechanisms and correlated diffusion events that reduce the activation energy barrier at concentrations as low as 1.5% V$^'_{Li}$. Our analyses of discretized diffusion events in both space and time illustrate the critical interplay between correlated dynamics, polarization and local distortion in promoting ionic conductivity in Li 3 OCl.

25 ENERGY STORAGE↗
DOE OSTI2021

Paradigms of frustration in superionic solid electrolytes

Superionic solid electrolytes have widespread use in energy devices, but the fundamental motivations for fast ion conduction are often elusive. In this Perspective, we draw upon atomistic simulations of a wide range of superionic conductors to illustrate some ways frustration can lower diffusion cation barriers in solids. Based on our studies of halides, oxides, sulfides and hydroborates and a survey of published reports, we classify three types of frustration that create competition between different local atomic preferences, thereby flattening the diffusive energy landscape. These include chemical frustration, which derives from competing factors in the anion–cation interaction; structural frustration, which arises from lattice arrangements that induce site distortion or prevent cation ordering; and dynamical frustration, which is associated with temporary fluctuations in the energy landscape due to anion reorientation or cation reconfiguration. For each class of frustration, we provide detailed simulation analyses of various materials to show how ion mobility is facilitated, resulting in stabilizing factors that are both entropic and enthalpic in origin. We propose the use of these categories as a general construct for classifying frustration in superionic conductors and discuss implications for future development of suitable descriptors and improvement strategies.

25 ENERGY STORAGE↗
DOE OSTI2018

Alloying effects on superionic conductivity in lithium indium halides for all-solid-state batteries

Alloying of anions is a promising engineering strategy for tuning ionic conductivity in halide-based inorganic solid electrolytes. We explain the alloying effects in Li 3 InBr 6-x Cl x , in terms of strain, chemistry, and microstructure, using first-principles molecular dynamics simulations and novel electronic structure analysis. We find that strain and bond chemistry can be tuned through alloying and affect the activation energy and maximum diffusivity coefficient. The similar conductivities of the x=3 and x=6 compositions can be understood by assuming the alloy separates into Brrich and Cl-rich regions. Phase-separation increases diffusivity at the interface and in the expanded Cl-region, suggesting microstructure effects are critical.

25 ENERGY STORAGE↗
DOE OSTI2017

Structural, Chemical, and Dynamical Frustration: Origins of Superionic Conductivity in closo -Borate Solid Electrolytes

Li 2 B 12 H 12 , Na 2 B 12 H 12 , and their closo-borate relatives exhibit unusually high ionic conductivity, making them attractive as a new class of candidate electrolytes in solid-state Li- and Na-ion batteries. However, further optimization of these materials requires a deeper understanding of the fundamental mechanisms underlying ultrafast ion conduction. To this end, we use ab initio molecular dynamics simulations and density-functional calculations to explore the motivations for cation diffusion. We find that superionic behavior in Li 2 B 12 H 12 and Na 2 B 12 H 12 results from a combination of key structural, chemical, and dynamical factors that introduce intrinsic frustration and disorder. A statistical metric is used to show that the structures exhibit a high density of accessible interstitial sites and site types, which correlates with the flatness of the energy landscape and the observed cation mobility. Furthermore, cations are found to dock to specific anion sites, leading to a competition between the geometric symmetry of the anion and the symmetry of the lattice itself, which can facilitate cation hopping. Finally, facile anion reorientations and other low-frequency thermal vibrations lead to fluctuations in the local potential that enhance cation mobility by creating a local driving force for hopping. In conclusion, we discuss the relevance of each factor for developing new ionic conductivity descriptors that can be used for discovery and optimization of closo-borate solid electrolytes, as well as superionic conductors more generally.

25 ENERGY STORAGE↗
DOE OSTI2016

Role of Dynamically Frustrated Bond Disorder in a Li + Superionic Solid Electrolyte

Inorganic lithium solid electrolytes are critical components in next-generation solid-state batteries, yet the fundamental nature of the cation-anion interactions and their relevance for ionic conductivity in these materials remains enigmatic. Here, we employ first-principles molecular dynamics simulations to explore the interplay between chemistry, structure, and functionality of a highly conductive Li + solid electrolyte, Li3InBr6. Using local-orbital projections to dynamically track the evolution of the electronic charge density, the simulations reveal rapid, correlated fluctuations between cation-anion interactions with different degrees of directional covalent character. These chemical bond dynamics are shown to correlate with Li + mobility, and are enabled thermally by intrinsic frustration between the preferred geometries of chemical bonding and lattice symmetry. We suggest that the fluctuating chemical environment from the polarizable anions functions similar to a solvent, contributing to the superionic behavior of Li 3 InBr 6 by temporarily stabilizing configurations favorable for migrating Li + . The generality of these conclusions for understanding solid electrolytes and key factors governing the superionic phase transition is discussed.

25 ENERGY STORAGE↗
DOE OSTI2015

Hole traps in sodium silicate: First-principles calculations of the mobility edge

Here, the structure and properties of (Na 2 O) 0.30 (SiO 2 ) 0.70 sodium silicate glass are studied by combined ab-initio and classical molecular dynamics simulations to identify the sources of electronic traps in the band gap. Structures from classical molecular dynamics melt-quench simulations are taken as initial configurations for first-principles density functional theory structural relaxation, from which electronic properties are determined. An ensemble of thirty glass structures, each containing 660 atoms, is prepared in order to perform statistical analyses. The inverse participation ratio is used as a metric to characterize localized states in the band gap and determine the mobility edge. Structures with varying amounts of local disorder (traps) are compared. The most localized and highest energy trap states are due to Si atoms with 2–3 non-bridging oxygen atoms. Control of the electronic properties of amorphous insulators and semiconductors is essential for the advancement of many technologies, such as photovoltaics and scintillators, for which the present analysis is indispensable.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Materials Modeling for High-Performance Radiation Detectors

Limitations in the performance of room-temperature gamma radiation detectors are intimately related to the properties of the materials used to fabricate them. In particular, atomic-scale defects in the materials that can depend on crystal growth or device processing conditions often ultimately determine the performance of a given detector. Understanding the origins and consequences of these various defects is essential to optimizing the materials and achieving maximum device performance (e.g., energy resolution and sensitivity). This project aims to enable rational materials design for select high-payoff challenges in radiation detection materials by using state-of-the-art modeling techniques bridging multiple resolutions. Three specific high-impact challenges are addressed: (i) design and optimization of electrical contact stacks for thallium bromide (TlBr) detectors to stabilize temporal response at room-temperature; (ii) design of host glass for large-volume, low-cost, high-performance glass scintillators; and (iii) determination of the electrical impacts of dislocation networks in cadmium zinc telluride (CZT) that limit its performance and usable single-crystal volume.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Exchange interactions of paramagnetic spins on α-Al 2 O 3

Superconducting qubits (SQs) represent a promising route to achieving a scalable quantum computer. However, the coupling between SQs and (as yet largely unidentified) parasitic noise sources has so far limited the functionality of current SQs by reducing the coherence time of the quantum states below a practical threshold for measurement and manipulation. These noise sources also more generally limit the performance of a variety of devices based on superconducting circuits, including high energy-resolution cryogenic radiation detectors, sensitive magnetic detectors (superconducting quantum interference devices, or SQUIDs), nanomechanical motion sensors and quantum-limited parametric amplifiers. In this paper, we study the origins of spin interactions on a substrate using OH on sapphire as a model system. We show how the substrate can mediate spin-spin interactions generally favoring FM interactions, but that strain can modify the interaction. Surface and sub-surface defects between the spin defects may also enhance the exchange interactions.

36 MATERIALS SCIENCE↗