A new framework for X-ray absorption spectroscopy data analysis based on machine learning: XASDAML

TL;DR

XASDAML is a comprehensive machine learning framework that streamlines X-ray absorption spectroscopy data analysis, enabling efficient large-scale data processing, pattern discovery, and property prediction with a user-friendly interface.

Contribution

It introduces a modular, integrated platform for XAS data analysis that combines dataset construction, ML modeling, statistical analysis, and visualization, adaptable to evolving research needs.

Findings

Successfully applied to copper datasets, demonstrating accurate property predictions.

Supports both supervised and unsupervised machine learning models.

Provides comprehensive statistical analysis and visualization tools.

Abstract

X-ray absorption spectroscopy (XAS) is a powerful technique to probe the electronic and structural properties of materials. With the rapid growth in both the volume and complexity of XAS datasets driven by advancements in synchrotron radiation facilities, there is an increasing demand for advanced computational tools capable of efficiently analyzing large-scale data. To address these needs, we introduce XASDAML,a flexible, machine learning based framework that integrates the entire data-processing workflow-including dataset construction for spectra and structural descriptors, data filtering, ML modeling, prediction, and model evaluation-into a unified platform. Additionally, it supports comprehensive statistical analysis, leveraging methods such as principal component analysis and clustering to reveal potential patterns and relationships within large datasets. Each module operates independently, allowing users to modify or upgrade modules in response to evolving research needs or technological advances. Moreover, the platform provides a user-friendly interface via Jupyter Notebook, making it accessible to researchers at varying levels of expertise. The versatility and effectiveness of XASDAML are exemplified by its application to a copper dataset, where it efficiently manages large and complex data, supports both supervised and unsupervised machine learning models, provides comprehensive statistics for structural descriptors, generates spectral plots, and accurately predicts coordination numbers and bond lengths. Furthermore, the platform streamlining the integration of XAS with machine learning and lowering the barriers to entry for new users.