Heterogeneity of solid electrolyte interphase layer sensitively determines thermo-chemo-mechanical stresses in a silicon anode particle

Kumar, Manoj; Sengupta, Aditi; Gupta, Kartik; Chakraborty, Jeevanjyoti; Das, Prasanta Kumar

doi:10.1007/s10853-022-07624-8

Cited by 4 publications

(2 citation statements)

References 100 publications

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“…Jiménez et al [ 62 ] and Li et al [ 63 ] tried to solve this issue by coating a robust artificial solid electrolyte interface (ASEI) layer on the silicon particle surface. Nevertheless, the development of an exemplary Solid Electrolyte Interphase (ASEI) layer necessitates the consideration of multiple critical factors, encompassing attributes such as thermal stability, ionic conductivity, and mechanical properties [ 64 ]. Applying the mathematical model of the ASEI layer to find key coefficients as descriptors to accelerate the design and development of ASEI film could be an effective method to reduce time and cost.…”

Section: Donations Of Descriptors In Li-cells’ Performance Improvemen...mentioning

confidence: 99%

“…Therefore, using such less accurate models as descriptors for machine learning can result in outputs that differ significantly from the target [ 66 ]. Manoj et al [ 64 ] provide a heterogeneous ASEI model to develop key parameters (SEI film thickness, the thickness of the inorganic–organic interface, elastic deformation and plastic deformation-related parameters etc.) for the ASEI layer.…”

Section: Donations Of Descriptors In Li-cells’ Performance Improvemen...mentioning

confidence: 99%

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Materials descriptors of machine learning to boost development of lithium-ion batteries

Wang,

Zhang

et al. 2024

Nano Convergence

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Traditional methods for developing new materials are no longer sufficient to meet the needs of the human energy transition. Machine learning (ML) artificial intelligence (AI) and advancements have caused materials scientists to realize that using AI/ML to accelerate the development of new materials for batteries is a powerful potential tool. Although the use of certain fixed properties of materials as descriptors to act as a bridge between the two separate disciplines of AI and materials chemistry has been widely investigated, many of the descriptors lack universality and accuracy due to a lack of understanding of the mechanisms by which AI/ML operates. Therefore, understanding the underlying operational mechanisms and learning logic of AI/ML has become mandatory for materials scientists to develop more accurate descriptors. To address those challenges, this paper reviews previous work on AI, machine learning and materials descriptors and introduces the basic logic of AI and machine learning to help materials developers understand their operational mechanisms. Meanwhile, the paper also compares the accuracy of different descriptors and their advantages and disadvantages and highlights the great potential value of accurate descriptors in AI/machine learning applications for battery research, as well as the challenges of developing accurate material descriptors. Graphical Abstract

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Section: Donations Of Descriptors In Li-cells’ Performance Improvemen...mentioning

confidence: 99%

Section: Donations Of Descriptors In Li-cells’ Performance Improvemen...mentioning

confidence: 99%

Materials descriptors of machine learning to boost development of lithium-ion batteries

Wang,

Zhang

et al. 2024

Nano Convergence

View full text Add to dashboard Cite

show abstract

Quantifying the solid electrolyte interphase stress induced capacity fading of lithium-ion batteries via a multiscale mechanical-electrochemical coupling model

He,

Xu,

Jiang

et al. 2024

Sci. China Technol. Sci.

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Electrochemo-mechanical response of all solid-state batteries: Finite element simulations supported by image-based 3D reconstruction of X-ray microscopy tomography

Huang

Gao

Guo

2023

Electrochimica Acta

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Heterogeneity of solid electrolyte interphase layer sensitively determines thermo-chemo-mechanical stresses in a silicon anode particle

Cited by 4 publications

References 100 publications

Materials descriptors of machine learning to boost development of lithium-ion batteries

Materials descriptors of machine learning to boost development of lithium-ion batteries

Quantifying the solid electrolyte interphase stress induced capacity fading of lithium-ion batteries via a multiscale mechanical-electrochemical coupling model

Electrochemo-mechanical response of all solid-state batteries: Finite element simulations supported by image-based 3D reconstruction of X-ray microscopy tomography

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