A new AutoML framework packages spectral featurization, interpretable modeling, and feature selection into a Python package and web app, demonstrated on XANES/PDF bond-length regression and grape sugar prediction.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.mtrl-sci 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data
A new AutoML framework packages spectral featurization, interpretable modeling, and feature selection into a Python package and web app, demonstrated on XANES/PDF bond-length regression and grape sugar prediction.