Data‐Driven Materials Innovation and Applications

Wang, Zhuo; Sun, Zhehao; Yin, Hang; Liu, Xinghui; Wang, Jinlan; Zhao, Haitao; Pang, Cheng Heng; Wu, Tao; Li, Shuzhou; Yin, Zongyou; Yu, Xue‐Feng

doi:10.1002/adma.202104113

Cited by 69 publications

(54 citation statements)

References 565 publications

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“…Optimization of active materials, electrolyte formulations, processing, and manufacturing of secondary batteries along the entire battery research chain 1 is a capital-, material-, and timeintensive task. 2 Consequently, there is only a limited number of data-driven [3][4][5] studies in battery research on in-house assembled 6 i.e. non-commercially acquired cells.…”

Section: Introductionmentioning

confidence: 99%

Robotic cell assembly to accelerate battery research

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Robotic cell assembly to accelerate battery research

et al. 2022

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“…Consequently, there is only a limited number of data-driven [3][4][5] studies in battery research on in-house assembled 6 i.e. non-commercially acquired cells.…”

Section: Introductionmentioning

confidence: 99%

Robotic cell assembly to accelerate battery research

Zhang

Fischer

Sanin

et al. 2022

Preprint

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Manual cell assembly confounds with research digitalization and reproducibility. Both are however needed for data-driven optimization of cell chemistries and charging protocols. Therefore, we present herein an automatic battery assembly system (AutoBASS) that is capable of assembling batches of up to 64 CR2023 cells. AutoBASS allows us to acquire large datasets on in-house developed chemistries and is herein demonstrated with NMC811 and Si@Graphite electrodes with a focus on formation and manufacturing data. The large dataset enables us to gain insights into the formation process through dQ/dV analysis and assess cell to cell variability. Exact robotic electrode placement provides a baseline for laboratory-scale manufacturing and reproducibility towards the accelerated translation of findings from the laboratory to the pilot plant scale.

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“…In the future, ML can not only be applied in the data processing of flexible sensor systems, but it would also further impact the design phase of flexible sensing systems concerning both the configuration [ 210 ] and materials [ 211 – 213 ]. Using inverse design realized by ML, an ideal material with desirable functionalities and an optimal sensing configuration with compact sensor implementations can be found, leading to a new generation of intelligent flexible sensing systems with powerful sensing performance [ 214 ].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Enhanced Flexible Mechanical Sensing

et al. 2023

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To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human–machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

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Data‐Driven Materials Innovation and Applications

Abstract: Figure 4. a,b) Schematic illustrations of decision tree for classification (a) and regression (b).

Cited by 69 publications

References 565 publications

Robotic cell assembly to accelerate battery research

Robotic cell assembly to accelerate battery research

Robotic cell assembly to accelerate battery research

Machine Learning-Enhanced Flexible Mechanical Sensing

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