Spectra-Scope : A toolkit for automated and interpretable characterization of material properties from spectral data

TL;DR

Spectra-Scope is an open-source AutoML toolkit that enables automated, interpretable analysis of spectral data for material property characterization, facilitating easier and more reliable spectroscopy analysis.

Contribution

The paper introduces Spectra-Scope, a novel AutoML framework that combines interpretability with ease of use for spectral data analysis in materials science.

Findings

Spectra-Scope achieves performance comparable to existing models.

It effectively handles diverse spectroscopy datasets.

The toolkit enhances understanding of spectral features and physical processes.

Abstract

Spectroscopy is a central pillar of materials characterization, providing useful information on properties like structure, composition, or excited state dynamics of a system. However, many spectroscopic techniques present challenges in development of interpretable, performant, and reliable supervised learning models due to the wide range of possible nonlinear correlations that can exist between the signal and the response variable (target) of interest. Here, we present Spectra-Scope, an open-source AutoML framework for automatic characterization of material properties from spectroscopy data using interpretable machine learning (ML) models. The software is implemented in Python and a no-code web application. It comprises tools for data preprocessing, nonlinear feature extraction, machine learning model training, and feature downselection. Users can easily train different types of simple, interpretable ML models on a set of feature transformations quickly and with modest computational resources. In this work, we outline the methods of Spectra-Scope and its effectiveness across diverse datasets, with applications to materials and agricultural spectroscopy data. We show that Spectra-Scope can reproduce performance of comparable models in the literature, and highlight how our emphasis on interpretability can be used to rationalize the behavior of individual models and understand the physical processes behind spectral features.