Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids

arXiv:2403.10042v1 Announce Type: cross
Abstract: Traditional analysis of highly distorted micro-X-ray diffraction ({\mu}-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data preprocessing and labeled experimental data. This study demonstrates the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations. We trained MTL models to identify phase information in {\mu}-XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models showed superior accuracy compared to binary …

abstract analysis application architecture arxiv cond-mat.mtrl-sci cs.lg data data preprocessing deep learning environments experimental identification micro multi-task learning multitask learning patterns process ray study type x-ray