Transforming X-ray Spectroscopy with Machine Learning

By Dr Silpaja Chandrasekar, PhDReviewed by Susha Cheriyedath, M.Sc.Jul 23 2024

In a paper published in the journal Machine Learning: Science and Technology, researchers highlighted the rise of computational spectroscopy as a vital tool for analyzing experimental spectra by emphasizing the synergy between experimental and theoretical advancements, particularly in short-wavelength techniques like core-hole X-radiation (X-ray) spectroscopies. The review detailed the shift from traditional wavefunction and density-functional methods to emerging machine-learning (ML) approaches, noting their potential to enhance speed and accuracy in X-ray spectral analysis.

Optimizing ML Representations

Optimal data representation is crucial in developing high-performing ML models for X-ray spectroscopy. Effective featurisation of X-ray absorption sites involves creating compact, relevant, and comprehensive descriptors that capture local atomic environments.

Methods like radial distribution curve (RDC), weighted atom-centered symmetry functions (wACSF), multiple scattering representations (MSR), and smooth overlap of atomic positions (SOAP) each offer different advantages in encoding structural and spectral information, balancing locality, invariance, and efficiency. However, challenges such as descriptor size and computational cost remain, which impact model performance and generalizability.

The many-body tensor representation (MBTR) integrates the 'bag-of-bonds' and Coulomb matrix approaches, enhancing feature vector accuracy by including bond lengths and angles. Compared to simpler descriptors, it has shown superior performance in predicting X-ray absorption near edge structure (XANES) spectra, though it faces challenges with vector length scaling.

Mapping Challenges Overview

ML models primarily focus on solving X-ray spectroscopy's forward and reverse mapping problems. The forward mapping task involves predicting spectroscopic observables like X-ray absorption or emission spectra from structural data, akin to quantum chemistry calculations.

Methods such as predicting X-ray photoelectron spectroscopy (XPS) spectra using descriptors like local many-body tensor representation (LMBTR) and smooth overlap of atomic positions (SOAP) have shown promise, especially for materials like solid-electrolyte interfaces. These approaches leverage ML to bridge computational prediction and experimental data gaps, often requiring extensive training data to achieve accuracy.

 Conversely, the reverse mapping problem, which translates experimental spectra back into structural properties, poses significant challenges. Traditional methods involve direct comparison to reference data, but these can be limited and require extensive datasets. Recent advances have included auto-encoder generative adversarial networks (AEGANs) with cycle consistency.

This approach ensures coherence between forward and reverse mappings, enhancing the accuracy and reliability of the ML models in X-ray spectroscopy. However, these complex networks come with their optimization challenges. Additionally, achieving broad applicability in ML models remains difficult due to the large-scale training data required, emphasizing the need for efficient data management and innovative techniques in ML.

Training Strategies

Accurate ML model performance in X-ray spectroscopy relies heavily on the quality and scale of training data. Current datasets, often derived from theoretical calculations due to insufficient experimental data, must be carefully developed and sampled. Key considerations include the computational level of theory, sampling approaches, and training strategies. Methods like random sampling, furthest-point sampling, similarity-based learning, and uncertainty-based active learning have shown varying results, with furthest-point sampling and curriculum learning yielding the best performance.

Model Interpretability Challenges

ML models in X-ray spectroscopy often function as black boxes, making it challenging to interpret their predictions. Understanding model behavior requires developing methods for both global and local explanations, such as variance threshold filters, feature importance, and Shapley analysis.

While approaches like Shapley analysis and graph neural networks (GNN) offer some insights by correlating predictions with physical properties, interpretability remains a complex issue, especially when dealing with correlated features and model complexity. Advances in attention mechanisms and feature attribution are promising, but further exploration is needed to enhance the transparency and utility of ML models in spectroscopy.

Measuring Prediction Uncertainty

Quantifying uncertainty in ML models for X-ray spectroscopy is crucial for evaluating prediction reliability. The primary sources of uncertainty are aleatoric, stemming from gaps or limitations in the training data, and epistemic, which arises from variability in the model itself. Common methods for uncertainty quantification include ensembling, Monte Carlo dropout, and bootstrap resampling. These techniques help estimate the range and reliability of predictions, with recent studies showing that uncertainty tends to increase with prediction quality and highlighting limitations such as underconfidence in certain scenarios.

ML Applications

ML methodologies enhance X-ray spectroscopy by addressing the computational challenges of large-scale simulations needed for systems with disorder or dynamic processes. These techniques streamline the analysis of X-ray spectra, which often requires extensive configurational snapshots from molecular dynamics simulations. Case studies include propane oxidation with size-selected Cux Pdy clusters, polyoxometalates (POMs) for energy storage, and Cox FeO4 materials for oxygen evolution reactions. ML also facilitates interpreting ultrafast X-ray pulse data at X-ray free electron lasers (X-FELs), improving insights into time-resolved experiments.

Conclusion

In summary, rapid advances in X-ray spectroscopy, driven by enhanced instrumentation and computational techniques, have significantly transformed the field. ML has made notable strides, improving forward and reverse mappings between X-ray spectra and structural data.

However, challenges remain in developing comprehensive training sets and addressing discrepancies between theoretical and experimental spectra. Continued progress in these areas promises to elevate the capabilities and applications of X-ray spectroscopy further.