4 papers
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…
Thermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning
Juejing Liu, Haydn Anderson, Noah I. Waxman +4
New discoveries in chemistry and materials science, with increasingly expanding volume of requisite knowledge and experimental workload, provide unique opportunities for machine le…
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…
Molecular Insights into Yb(III) Speciation in Sulfate-Bearing Hydrothermal Fluids from X-ray Absorption Spectra Informed by ab initio Molecular Dynamics
Xiaodong Zhao, Duo Song, Sebastian Mergelsberg +9
Rare earth elements (REEs) are critical for advanced technologies, yet in hydrothermal aqueous solutions the molecular level details of their interaction with ligands that control…