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Atomic Absorption Spectroscopy: Overview01:27

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Atomic absorption spectroscopy (AAS) is a technique used to analyze elements by measuring electromagnetic radiation (EMR) absorbed by atoms, which causes them to transition to a higher-energy orbit. The most crucial step in AAS is atomization, where the analyte is converted into gas-phase atoms, typically through a flame or furnace. Some of these atoms become thermally excited in the flame, while most remain in the ground state.
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Atomic spectroscopy is a vital tool in elemental analysis, both qualitatively and quantitatively. It can be broadly divided into optical spectroscopy, mass spectroscopy, and X-ray spectroscopy methods. The optical spectroscopic methods are atomic absorption spectroscopy (AAS), atomic emission spectroscopy (AES), and atomic fluorescence spectroscopy (AFS). The first step in all three methods is atomization, where the solid, liquid, or solution-phase samples are converted into gas-phase atoms and...
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Atomic Emission Spectroscopy: Overview01:20

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Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
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Atomic Absorption Spectroscopy: Radiation and Light Sources01:13

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Atomic absorption spectroscopy (AAS) relies on the Beer-Lambert law, which requires that the radiation source emits a narrow range of wavelengths to match the absorption characteristics of the analyte atom. The primary criteria for choosing an appropriate radiation source in AAS is to provide a precise and intense emission at specific wavelengths that will allow accurate detection of the analyte.
Two common narrow-range 'line' sources used in AAS are hollow-cathode lamps (HCLs) and...
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Atomic Absorption Spectroscopy: Lab01:21

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For AAS measurements, samples must be introduced as clear solutions, often requiring extensive preliminary treatment to dissolve materials like soils, animal tissues, and minerals. Common methods for sample preparation include treatment with hot mineral acids, wet ashing, combustion in closed containers, high-temperature ashing, or fusion with reagents.
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Atomic Absorption Spectroscopy: Instrumentation01:22

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An atomic absorption spectrophotometer (AAS) comprises several components: a radiation source, an atomizer, a monochromator, and a detector. The radiation source can be a hollow-cathode lamp (HCL) or an electrodeless-discharge lamp (EDL), both of which provide a narrow emission line of the required wavelength. However, some instruments use continuum sources and high-resolution monochromators to achieve a narrow range of radiation.
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  3. From Local Atomic Structure To X-ray Spectra: Absorber-centric Machine-learning Encoding.
  1. Home
  3. From Local Atomic Structure To X-ray Spectra: Absorber-centric Machine-learning Encoding.

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

Thomas James Pope1, Bowen Li2, Hendrik Junkawitsch3,4

  • 1Chemistry - School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K.

The Journal of Physical Chemistry. A
|May 6, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning now predicts X-ray spectroscopy (XRS) more accurately by considering the entire molecular environment, not just the absorbing atom. This approach accelerates simulations for diverse chemical systems.

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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.