Role of anisotropy in determining stability of electrodeposition at solid-solid interfaces

Ahmad, Zeeshan; Viswanathan, Venkatasubramanian

doi:10.1103/physrevmaterials.1.055403

Cited by 29 publications

(40 citation statements)

References 53 publications

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“…6H, bottom). Recently, a previous-ly unexplored regime was assessed in recent studies by Ahmad and Viswanathan (185,186); in this regime, v c is smaller than v m , which is usually the case for inorganic solid electrolyte. Under these conditions, hydrostatic stresses destabilize the interface, and stable deposition is achieved at low electrolyte modulus (i.e., G S /G M < 0.7, where G M is the shear modulus of the metal).…”

Section: Approaches For Regulating Morphology Of Metal Electrodepositsmentioning

confidence: 99%

Controlling electrochemical growth of metallic zinc electrodes: Toward affordable rechargeable energy storage systems

2021

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Scalable approaches for precisely manipulating the growth of crystals are of broad-based science and technological interest. New research interests have reemerged in a subgroup of these phenomena—electrochemical growth of metals in battery anodes. In this Review, the geometry of the building blocks and their mode of assembly are defined as key descriptors to categorize deposition morphologies. To control Zn electrodeposit morphology, we consider fundamental electrokinetic principles and the associated critical issues. It is found that the solid-electrolyte interphase (SEI) formed on Zn has a similarly strong influence as for alkali metals at low current regimes, characterized by a moss-like morphology. Another key conclusion is that the unique crystal structure of Zn, featuring high anisotropy facets resulting from the hexagonal close-packed lattice with a c/a ratio of 1.85, imposes predominant influences on its growth. In our view, precisely regulating the SEI and the crystallographic features of the Zn offers exciting opportunities that will drive transformative progress.

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Section: Approaches For Regulating Morphology Of Metal Electrodepositsmentioning

confidence: 99%

Controlling electrochemical growth of metallic zinc electrodes: Toward affordable rechargeable energy storage systems

2021

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“…For anisotropic contact, an interface was characterized by the directions of Li metal normal to it and the low‐index facets of the solid electrolyte. During the screening process, the authors trained GBR and KRR using the DFT elastic tensors for 2401 unique surfaces of 482 electrolyte materials obtained by applying rotating axes and transformation rules to full elastic tensors from Materials Project, and then predicted the elastic tensors of 548 cubic crystal structures with each having 3 unique surfaces as well as the stability parameters corresponding to 4 distinct directions of Li metal for each surface. The models predicted twenty dendrite‐suppressing interfaces formed by four solid electrolytes (Li 2 WS 4 ‐

P \bar{4} 2 m

, LiBH 4 ‐

P \bar{1}

, LiOH‐ P 4/ nmm , and Li 2 WS 4 ‐

I \bar{4} 2 m

) and Li.…”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

399

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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“…The change in the electrochemical potential at a given k is given by Δμ e – ( k ) = χ( k )[ f 1 ( k ) cos( kx ) + f 2 ( k ) sin( kx )]. 54 This serves to define the stability parameter χ( k ) as Δμ e – can be obtained by including the effect of mechanical stresses and surface tension on the electrochemical potential of the species at a deformed interface as 43 From eq 2 , Δμ e – depends on k through the surface tension γ, curvature κ, the hydrostatic stress Δp , and deviatoric stress τ generated at the interface. e and s in the subscripts refer to the metal electrode (anode) and solid electrolyte, respectively; V M is the molar volume of the metal atom in the anode, v the ratio of molar volume of the metal ion in electrolyte V M z + to the metal atom in the anode V M , z the valence of the metal, and e n the normal to the interface pointing toward the electrolyte.…”

Section: Results and Discussionmentioning

confidence: 99%

“…Previously, we also observed a large variation in the stability parameter, with the [010] orientation of Li and solid electrolyte at the interface being the most compliant. 54 Here, we analyze the stability of electrodeposition at an anisotropic interface involving crystalline solid electrolyte in contact with crystalline Li metal.…”

Section: Results and Discussionmentioning

confidence: 99%

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Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

et al. 2018

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Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12 000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large data set for screening, we use machine-learning models to predict the mechanical properties of several new solid electrolytes. The machine-learning models are trained on purely structural features of the material, which do not require any first-principles calculations. We train a graph convolutional neural network on the shear and bulk moduli because of the availability of a large training data set with low noise due to low uncertainty in their first-principles-calculated values. We use gradient boosting regressor and kernel ridge regression to train the elastic constants, where the choice of the model depends on the size of the training data and the noise that it can handle. The material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and sublattice electronegativity. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and four solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.

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Role of anisotropy in determining stability of electrodeposition at solid-solid interfaces

Cited by 29 publications

References 53 publications

Controlling electrochemical growth of metallic zinc electrodes: Toward affordable rechargeable energy storage systems

Controlling electrochemical growth of metallic zinc electrodes: Toward affordable rechargeable energy storage systems

A Critical Review of Machine Learning of Energy Materials

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

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