Synchrotron Imaging of Pore Formation in Li Metal Solid-State Batteries Aided by Machine Learning

Dixit, Marm; Verma, Ankit; Zaman, Wahid; Zhong, Xinlin; Kenesei, Péter; Park, Jun-Sang; Almer, Jonathan; Mukherjee, Partha P.; Hatzell, Kelsey B.

doi:10.1021/acsaem.0c02053

Cited by 84 publications

(68 citation statements)

References 32 publications

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“…The green phase is the identified lithium metal while the blue phase is the identified pore/void phases. Reproduced with permission from (Dixit et al, 2020). Copyright 2020 American Chemical Society.…”

Section: Discussion and Perspectivementioning

confidence: 99%

“…To assist the interpretation of in-situ Li metal morphological transformations during galvanostatic cycling in Li|LLZO|Li cells, Dixit et al trained a convolution ANN and observed non-uniform Li electrode kinetics at both electrodes during cycling (Figure 11). The hot spots in Li metal are correlated with microstructural anisotropy in LLZO (Dixit et al, 2020). Advanced visualization combined with electrochemistry represents an important strategy to resolve non-equilibrium effects that limit rate capabilities of SSBs.…”

Section: Interfaces and Coatingsmentioning

confidence: 99%

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Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

et al. 2021

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Materials for solid-state batteries often exhibit complex chemical compositions, defects, and disorder, making both experimental characterization and direct modeling with first principles methods challenging. Machine learning (ML) has proven versatile for accelerating or circumventing first-principles calculations, thereby facilitating the modeling of materials properties that are otherwise hard to access. ML potentials trained on accurate first principles data enable computationally efficient linear-scaling atomistic simulations with an accuracy close to the reference method. ML-based property-prediction and inverse design techniques are powerful for the computational search for new materials. Here, we give an overview of recent methodological advancements of ML techniques for atomic-scale modeling and materials design. We review applications to materials for solid-state batteries, including electrodes, solid electrolytes, coatings, and the complex interfaces involved.

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Section: Discussion and Perspectivementioning

confidence: 99%

Section: Interfaces and Coatingsmentioning

confidence: 99%

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

et al. 2021

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“…Interfacial kinetics heterogeneity at the Li metal solid electrolyte interface initiates several degradation pathways including filament formation limiting the stability and performance of solid-state batteries. In addition to growth of filaments, high rate electrodissolution from the Li metal can lead to formation of pores that can cause onset of failure [75]. A direct evidence of this was obtained from X-ray tomography measurements of Li | LLZO | Li symmetric cells (Figure 5c).…”

Section: Anode Materials and Designs For Ssbmentioning

confidence: 94%

“…(c) Porosity for two lithium metal electrodes as a function of cycling steps obtained from X-ray tomography measurements and machine learning segmentation. Reprinted with permission from[75]. (d) Silicon | LPS | Li cell cycling behavior.…”

mentioning

confidence: 99%

Current Status and Prospects of Solid-State Batteries as the Future of Energy Storage

Dixit¹,

Muralidharan²,

Parejiya³

et al. 2022

Management and Applications of Energy Storage Devices

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Solid-state battery (SSB) is the new avenue for achieving safe and high energy density energy storage in both conventional but also niche applications. Such batteries employ a solid electrolyte unlike the modern-day liquid electrolyte-based lithium-ion batteries and thus facilitate the use of high-capacity lithium metal anodes thereby achieving high energy densities. Despite this promise, practical realization and commercial adoption of solid-state batteries remain a challenge due to the underlying material and cell level issues that needs to be overcome. This chapter thus covers the specific challenges, design principles and performance improvement strategies pertaining to the cathode, solid electrolyte and anode used in solid state batteries. Perspectives and outlook on specific applications that can benefit from the successful implementation of solid-state battery systems are also discussed. Overall, this chapter highlights the potential of solid-state batteries for successful commercial deployment in next generation energy storage systems.

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“…An example of this from Dixit et al contrasted segmentation results from the commonly used Otsu thresholding (binary) algorithm and a convolutional neural network on reconstructions from X-ray tomography on a symmetric Li|LLZO (Li 7 La 3 Zr 2 O 12 )|Li cell. [120] The neural network architecture was trained on 800 images starting with manual labeling and was crucial in being able to distinguish Li metal from surrounding pores that form during cycling. The resulting segmentation employing the neural network also allows for quantitative analysis of porosity during plating and stripping.…”

Section: Quantitative Imaging-based Techniquesmentioning

confidence: 99%

A Review of Existing and Emerging Methods for Lithium Detection and Characterization in Li‐Ion and Li‐Metal Batteries

Paul

McShane

Colclasure

et al. 2021

Advanced Energy Materials

140

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Whether attempting to eliminate parasitic Li metal plating on graphite (and other Li-ion anodes) or enabling stable, uniform Li metal formation in 'anode-free' Li battery configurations, the detection and characterization (morphology, microstructure, chemistry) of Li that cannot be reversibly cycled is essential to understand the behavior and degradation of rechargeable batteries. In this review, various approaches used to detect and characterize the formation of Li in batteries are discussed. Each technique has its unique set of advantages and limitations, and works towards solving only part of the full puzzle of battery degradation. Going forward, multimodal characterization holds the most promise towards addressing two pressing concerns in the implementation of the next generation of batteries in the transportation sector (viz. reducing recharging times and increasing the available capacity per recharge without sacrificing cycle life). Such characterizations involve combining several techniques (experimental-and/or modeling-based) in order to exploit their respective advantages and allow a more comprehensive view of cell degradation and the role of Li metal formation in it. It is also discussed which individual techniques, or combinations thereof, can be implemented in real-world battery management systems on-board electric vehicles for early detection of potential battery degradation that would lead to failure.

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Synchrotron Imaging of Pore Formation in Li Metal Solid-State Batteries Aided by Machine Learning

Cited by 84 publications

References 32 publications

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

Accelerated Atomistic Modeling of Solid-State Battery Materials With Machine Learning

Current Status and Prospects of Solid-State Batteries as the Future of Energy Storage

A Review of Existing and Emerging Methods for Lithium Detection and Characterization in Li‐Ion and Li‐Metal Batteries

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