From Local Atomic Structure to X-ray Spectra: Absorber-Centric Machine-Learning Encoding.

Area of Science:

• Computational Chemistry
• Materials Science
• Spectroscopy

Background:

• X-ray spectroscopy (XRS) offers element-specific structural insights but is computationally intensive for complex molecules.
• Existing machine learning (ML) methods for XRS prediction often use simplified descriptors and struggle with chemical diversity.

Purpose of the Study:

• To develop a novel ML framework for accurate and efficient X-ray spectroscopy prediction.
• To improve the transferability and robustness of ML models across diverse molecular systems.

Main Methods:

• Introduced an environment-aware nuclear structure representation beyond the absorbing atom.
• Integrated a physically motivated, multiscale Gaussian spectral basis with ridge projection.
• Employed a multiscale structural similarity loss for enhanced robustness.

Main Results:

• Achieved accurate and transferable XRS predictions across various molecular geometries and chemical environments.
• Demonstrated the physical interpretability of the ML model's predictions.
• Established a scalable route for machine-learned X-ray spectroscopy.

Conclusions:

• The proposed ML framework significantly advances structure-to-spectrum prediction for XRS.
• This approach offers a computationally efficient and physically grounded alternative to traditional simulations.
• The method holds promise for broader applications in materials science and chemistry.