Multivariate hyperspectral data analytics across length scales to probe compositional, phase, and strain heterogeneities in electrode materials

Chemistry–mechanics–geometry coupling in positive electrode materials: a scale-bridging perspective for mitigating degradation in lithium-ion batteries through materials design

“…5A showcases a deep learning model based on the Mask-RCNN algorithm, which performs the image segmentation task on a polydisperse ensemble of positive electrode particles imaged by X-ray spectromicroscopy. 93,96 The combination of this segmentation algorithm with chemical imaging enables the colocalization of morphology with state-ofcharge and phase coexistence across several length-scales. 96 The intention of this type of workow is to pinpoint how the chemomechanical behavior of single-particles relates to their geometry and inuences ensemble reaction heterogeneities.…”

Machine learning accelerates identification of lithiated phases in X-ray images of battery hosts

“…In the latest study, Santos et al. (2022) 4 propose a machine learning approach to sift through such large datasets of V 2 O 5 electrodes and accurately identify the underlying lithiated phases.…”

“…The approach of Santos et al. 4 and its aforementioned extensions can help understand many such mechanisms. For example, the coupled effects of lithiation and mechanical stress have been described in many different ways in the literature.…”

“…For example, the coupled effects of lithiation and mechanical stress have been described in many different ways in the literature. 4 , 5 , 7 Each of these descriptions for lithiation–mechanics coupling predicts a distinct lithiation dynamics for the same operation. We can combine such predictions with the observations of lithiation fields to characterize the lithiation–mechanics behavior in the intercalation material being examined.…”

Data science enables X-ray vision

“…From the perspective of productivity, the collaboration has led to several important publications in which we take great pride. 1 , 2 , 3 Our article in Patterns 4 is both a celebration of this work and an elevation of our approaches to new levels through the incorporation of data-driven methods. On a more personal level, it has been immensely rewarding to see the collaboration succeed across several “generations” of graduate students, post-doctoral researchers, and staff scientists.…”