3 papers
cs.LG2025
OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions
Chris Young, Juejing Liu, Marie L. Mortensen +6
The proliferation of spectroscopic data across various scientific and engineering fields necessitates automated processing. We introduce OASIS (Omni-purpose Analysis of Spectra via…
cond-mat.mtrl-sci2025
Data-Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data
Juejing Liu, Xiaoxu Li, Yifu Feng +4
Quantitative understanding of rare earth element (REE) mineralization mechanisms, crucial for improving industrial separation, remains limited. This study leverages 1239 hydrotherm…
cond-mat.mtrl-sci2024
Accurate and Data-Efficient Micro-XRD Phase Identification Using Multi-Task Learning: Application to Hydrothermal Fluids
Yanfei Li, Juejing Liu, Xiaodong Zhao +4
Traditional analysis of highly distorted micro-X-ray diffraction (μ-XRD) patterns from hydrothermal fluid environments is a time-consuming process, often requiring substantial data…