Self‐Healing Mechanism of Lithium in Lithium Metal

Jiao, Junyu; Lai, Genming; Zhao, Lang; Lu, Jiaze; Li, Qidong; Xu, Xianqi; Jiang, Yao; He, Yan‐Bing; Ouyang, Chuying; Pan, Feng; Li, Hong; Zheng, Jiaxin

doi:10.1002/advs.202105574

Cited by 36 publications

(51 citation statements)

References 64 publications

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“…The data of the pure Li metal system is from ref. [22] and the data of the pure Cu metal system is from ref. [29].…”

Section: Resultsmentioning

confidence: 99%

“…[7,11,12] However, the problems of low coulombic measurements. [22] However, up to now, no ML-based Li-Cu interface potential was reported that could achieve the accuracy of ab initio calculations.…”

Section: Introductionmentioning

confidence: 99%

“…[ 21 ] We recently developed a Li surface potential model based on deep neural networks to achieve a large‐scale simulation of Li deposition, which has a high accuracy closed to DFT calculations and experimental measurements. [ 22 ] However, up to now, no ML‐based Li–Cu interface potential was reported that could achieve the accuracy of ab initio calculations.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Neural Network Interface Potential for Li‐Cu Systems

Lai

Jiao

Fang

et al. 2022

Adv Materials Inter

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Copper foil is one of the most commonly used current collector materials in Li metal batteries. However, many problems on the Li–Cu interface have not been effectively solved due to the lack of a fundamental understanding of Li–Cu interaction at the atomic scale. In this work, a deep neural network interface potential for Li‐Cu systems using neural networks combined with active learning strategies is developed. The potential shows excellent performances on the energy and force calculations, physical properties predictions, and structure explorations. Moreover, the study of the Li adsorption behaviors on the Cu surface demonstrates the accuracy of this potential in the investigation of the Li–Cu interface. This potential for the Li‐Cu systems provides an important opportunity to advance the understanding of interface problems in Li metal batteries.

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“…The data of the pure Li metal system is from ref. [22] and the data of the pure Cu metal system is from ref. [29].…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Neural Network Interface Potential for Li‐Cu Systems

Lai

Jiao

Fang

et al. 2022

Adv Materials Inter

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“…Currently, a widely used option is to minimise the impact of outages by introducing self-healing mechanisms [15]. In any case, the use of this type of mechanism implies that they are strongly meshed (radial and redundant topology) and are able to isolate a faulty section until the problem is solved without the need for the entire network to be out of service.…”

Section: Environmentmentioning

confidence: 99%

Detection and Classification of Fault Types in Distribution Lines by Applying Contrastive Learning to GAN Encoded Time-Series of Pulse Reflectometry Signals

et al. 2022

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This study proposes a new method for detecting and classifying faults in distribution lines. The physical principle of classification is based on time-domain pulse reflectometry (TDR). These high-frequency pulses are injected into the line, propagate through all of its bifurcations, and are reflected back to the injection point. According to the impedances encountered along the way, these signals carry information regarding the state of the line. In the present work, an initial signal database was obtained using the TDR technique, simulating a real distribution line using (PSCAD™). By transforming these signals into images and reducing their dimensionality, these signals are processed using convolutional neural networks (CNN). In particular, in this study, contrastive learning in Siamese networks was used for the classification of different types of faults (ToF). In addition, to avoid the problem of overfitting owing to the scarcity of examples, generative adversarial neural networks (GAN) have been used to synthesise new examples, enlarging the initial database. The combination of Siamese neural networks and GAN allows the classification of this type of signal using only synthesised examples to train and validate and only the original examples to test the network. This solves the problem of the lack of original examples in this type of signal of natural phenomena which are difficult to obtain and simulate.

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“…8 Li nucleation and dendrite growth also occurs on Li metal anodes. 9,10 In order to improve the performance of Li-ion batteries and gain more insight into this phenomenon, the process of Li nucleation has been extensively studied both experimentally and computationally in the recent years. [11][12][13] Li nucleation is thermodynamically possible when the graphite electrode is at a lower potential (voltage) with respect to the Li/Li + reference electrode.…”

Section: Introductionmentioning

confidence: 99%