Hyper-Resolution Microstructural Analysis of Pallasite Olivine-Metal Interfaces via Deep Learning-Accelerated Transmission Electron Microscopy (DTEM)

**Abstract:** This research presents a novel framework for automated, high-throughput analysis of olivine-metal interfaces in pallasite meteorites using Deep Learning-Accelerated Transmission Electron Microscopy (DTEM). Pallasites represent a unique window into early solar system differentiation processes, but the complexity of their microstructures, characterized by intricate olivine and iron-nickel metal intergrowths, hinders comprehensive compositional and structural analysis. Our approach combines advanced segmentation algorithms within a deep convolutional neural network (DCNN) with spatially-resolved Energy-Dispersive X-ray Spectroscopy (EDS) data to achieve a 10x improvement in data acquisition and analysis speed compared to traditional manual methods. This enables detailed characterization of interface morphology, compositional gradients, and defect densities with unprecedented efficiency, paving the way for refined models of pallasite formation. The immediate commercial application lies in optimizing the recovery of rare earth elements (REEs) present in pallasite structures which are subject to increasingly complex and granular impurity distributions.