Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions

TL;DR

This paper demonstrates that combining X-ray absorption near-edge spectra and pair distribution functions using interpretable machine learning effectively reveals local structural and chemical environments in oxides, with XANES often providing dominant information.

Contribution

The study introduces a multimodal, interpretable machine learning approach that integrates XANES and PDFs to analyze local structures, highlighting the relative strengths of each modality.

Findings

XANES generally outperforms PDFs in structural predictions

Differential PDFs narrow the performance gap between modalities

Combining XANES and PDFs often yields the best insights

Abstract

We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and chemical environments of transition metal cations in oxides. Random forest models were trained on simulated XANES, PDF, and both combined to extract oxidation state, coordination number, and mean nearest-neighbor bond length. XANES-only models generally outperformed PDF-only models, even for structural tasks, although using the metal's differential PDFs (dPDFs) instead of total PDFs narrowed this gap. When combined with PDFs, information from XANES often dominates the prediction. Our results demonstrate that XANES contain rich structural information and highlight the utility of species-specificity. This interpretable, multimodal approach is quick to implement with suitable databases and offers valuable insights into the relative strengths of different modalities, guiding researchers in experiment design and identifying when combining complementary techniques adds meaningful information to a scientific investigation.