HomeTechnologyArtificial intelligenceCharacterizing Battery Electrodes with Machine Learning

Characterizing Battery Electrodes with Machine Learning

In an article that recently appeared in the magazine ACS Energy Lettersresearchers discussed the advanced characterization of battery electrodes powered by machine learning.

Study: Machine learning powered advanced characterization of battery electrodes. Image credit: Chesky/Shutterstock.com

Background

Numerous material characteristics with geographically and time variable chemistry, morphology and crystallography are related to the performance of lithium (Li)-ion batteries. Since Li-ion batteries were first introduced to the market, they have made significant progress as a result of mastering these properties to obtain favorable cell performance.

Sophisticated characterization techniques are often required to confirm management of material properties, but there are still many things that analytical equipment cannot practically or physically measure. Artificial intelligence (AI) techniques to artificially enhance, classify, integrate, predict or create data have improved significantly over the past decade.

Many commercial data analysis software programs have begun to incorporate AI approaches, for example to improve correlation data through multimodal techniques and spatial resolution or to recognize and quantify patterns in the data. Every year new methods of using AI to improve data are demonstrated, and many of these methods are soon being used in commercial applications. A particularly pertinent illustration is the fact that datasets are generated using AI approaches with more information than any characterization technique can, exceeding the capabilities of the hardware. Despite the progress made so far, there is still plenty of room to use AI techniques to improve our characterization capabilities, giving researchers the ability to measure and manage battery material qualities otherwise impossible to get.

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About the study

In this study, the authors discussed the performance limitations of lithium-ion battery electrodes and how they require an understanding of material characterization. They also explained the significant advances in lab-based characterization techniques and the structure-function relationship of electrodes.

The team noted that by leveraging data collected through different methodologies, AI has shown significant potential for achieving representative, 3D, multimodal datasets. A summary was given of the current developments in laboratory-based characterization methods for Li-ion electrodes. They discussed how AI methodologies could integrate and improve these methods, significantly increasing the understanding of electrodes.

The researchers provided an overview of the relevant battery material properties at multiple length scales, discussed the drawbacks of lab-based material characterization methods, and also discussed how recent advances in AI can be applied to overcome these drawbacks. From this perspective, the systemic limitations of microscopy techniques could be overcome by combining multimodal data with generative hostile networks (GANs). Furthermore, a scenario has been outlined in which AI techniques make it possible to combine data into one multimodal dataset that covers different length scales.

Observations

To use transmission electron microscopy (TEM) methods, samples typically need to be thinner than 100 nm or less than 10 nm for atomic resolution. The sample slice must then be micro-manipulated for mounting and imaging. Another example of sample preparation limitation was the requirement that nano-X-ray computed tomography (XCT) samples be only slightly wider than the field of view, or about 0.1 mm, to achieve the best attenuation. It was impossible to perform correlative metrology using different procedures and length scales or to establish statistical confidence in the methodology due to the difficulties of sample preparation.

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The time required to cut small samples has decreased significantly after the recent introduction of laser milling in electron microscopes. Systematic barriers that prevented the simultaneous collection of the many performance-affecting material attributes could be overcome by AI-enhanced characterization. Creating datasets with greater resolution and greater dimensionality was made possible by CNN-based approaches.

In addition, information collected from all characterization methods can be combined to create unified representative volumes of electrode microstructure. These unified volumes could support in-depth multiphysical simulations and high-throughput optimization when combined with electrochemical data.

Although Li-ion electrodes were used as a specific example, the general methodology was broadly relevant to several areas of energy materials research. High-quality data would be essential to the success of the new characterization paradigm that provided these opportunities. Datasets that can be read or understood by humans were not needed for neural networks. The full potential of data-driven ML was not realized when the networks were constrained to output data in formats such as 3D volumes representing different material qualities so that physical processes could be boosted on them.

Conclusions

In conclusion, this study has made it clear that more abstract models can be created using data from the entire process, from creation to demise, if the image data is integrated with other data streams. These models would omit the constraints necessary to produce findings that can be interpreted by humans, as well as the assumptions and simplifications included in the models.

The authors mentioned that achieving this goal requires careful thought and planning about relevant variables, as well as data collection, organization, and processing. They believe that a deeper understanding of the numerous physical heterogeneities in the materials is necessary for future advances.

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References

Finegan, DP, Squires, I., Dahari, A., et al. Machine learning-driven advanced characterization of battery electrodes. ACS Energy Letters 7, 4368-4378 (2022).

https://pubs.acs.org/doi/10.1021/acsenergylett.2c01996

Disclaimer: The views expressed here are those of the author, expressed in their personal capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork, the owner and operator of this website. This disclaimer forms part of the terms of use of this website.

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