This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

CdSe/CdS core/shell nanorods can exhibit high photoluminescence quantum yields, but it is not yet clear what processes determine the yields and how they can be controlled. Moreover, the effective band alignment between the core and the shell affects quantum yield, but its nature is still under debate. We systematically studied quantum yields when the shell is excited as a function of both core size and shell volume. Using time-resolved photoluminescence decay measurements and transient-absorption spectroscopy, we found that quantum yields are determined by a balance between radiative and nonradiative recombination rates, and not by single-carrier trapping. The radiative recombination rate decreases as the nanorod volume increases, independent of the core size. The results indicate that high quantum yields can be obtained only by limiting the size of the shell and point to an effective quasi-type-II band alignment for all of the nanorods in this study.

The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.