Learning material physics from images of battery primary particles

With the development of advanced synchrotron light sources, we have an unprecedented capability to image the spatio-dynamics of lithium-ion battery materials, allowing us to directly observe lithium (de)insertion during charge and discharge. However, data-driven analysis to extract hidden information in full images remains uncharted territory. Combining in-situ scanning transmission x-ray microscopy (STXM) images of lithium iron phosphate (LFP) nanoparticles[1], phase-field models[2], and the recently developed framework for the inverse learning of physical models [3-4], we extract the free energy and reaction kinetics of LFP, a phase-separating material, which is corroborated by theory. We also simultaneously invert the spatial heterogeneity and further validate it by multimodal imaging of the same particles. The result demonstrates the possibility of learning physical quantities that are otherwise difficult to measure and offers a new approach to imaging surface heterogeneity.

[1] Lim et al. "Origin and hysteresis of lithium compositional spatiodynamics within battery primary particles." Science (2016)

[2] Bazant, "Theory of chemical kinetics and charge transfer based on nonequilibrium thermodynamics." Accounts of chemical research (2013)

[2] Zhao el al. "Image inversion and uncertainty quantification for constitutive laws of pattern formation." Journal of Computational Physics (2021)

[2] Zhao et al. "Learning the physics of pattern formation from images." Physical review letters (2020)