# Integrating neutron vibrational spectroscopy and computer simulation to elucidate structure and dynamics of hydrogen

**Authors:** Yongqiang Cheng, Anibal J. Ramirez-Cuesta, Murillo L. Martins, Chang Liu

PMC · DOI: 10.1063/4.0000790 · Structural Dynamics · 2026-03-02

## TL;DR

This paper explains how neutron spectroscopy and computer simulations help study hydrogen's structure and movement, which is hard to observe with other methods.

## Contribution

The paper highlights recent advances in combining neutron vibrational spectroscopy with computational methods for hydrogen-containing materials.

## Key findings

- Neutron vibrational spectroscopy provides unique insights into hydrogen structure and dynamics.
- Combining this technique with computer simulations reveals details inaccessible by other methods.
- Recent machine learning progress offers new opportunities to enhance these methods.

## Abstract

Understanding the structure and dynamics of hydrogen is critically important, yet direct experimental measurements are often challenging. Hydrogen interacts only weakly with common probing particles such as photons and electrons, and strong nuclear quantum effects can produce large nonthermal and anisotropic atomic displacements. Neutron scattering, however, provides a uniquely powerful approach due to the strong and distinct interactions of neutrons with atomic hydrogen, molecular hydrogen, and deuterium. Beyond neutron diffraction, which enables direct determination of hydrogen and deuterium positions, neutron vibrational spectroscopy—particularly when combined with computer simulations and modeling—offers unparalleled insights into hydrogen structure and dynamics that are inaccessible by other techniques. In this paper, after briefly summarizing the theoretical foundations, we review recent advances in applying neutron vibrational spectroscopy and computational methods to hydrogen-containing materials, ranging from molecular hydrogen adsorption to organic, inorganic, and hybrid compounds with diverse hydrogen local structure. Finally, we discuss opportunities offered by the recent progress in machine learning to further enhance the capabilities of this method.

## Linked entities

- **Chemicals:** hydrogen (PubChem CID 783), deuterium (PubChem CID 24523), molecular hydrogen (PubChem CID 783)

## Full-text entities

- **Diseases:** HYDROGEN COMPOUNDS (MESH:D005597)
- **Chemicals:** H (MESH:D006859), Mn (MESH:D008345), Zr (MESH:D015040), CuZn-MFU-4l (-), 2H (MESH:D003903), silica (MESH:D012822), proton (MESH:D011522), acetylene (MESH:D000114), palladium hydride (MESH:C032391), water (MESH:D014867), lithium (MESH:D008094), metal (MESH:D008670), Zn (MESH:D015032), oxygen (MESH:D010100), NH3 (MESH:D000641), nitrogen (MESH:D009584), MOFs (MESH:D000073396), carbon (MESH:D002244), perovskite (MESH:C059910)
- **Cell lines:** HKUST-1 — Mus musculus (Mouse), Hybridoma (CVCL_C7RB)

## Full text

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## Figures

10 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12956373/full.md

## References

53 references — full list in the complete paper: https://tomesphere.com/paper/PMC12956373/full.md

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Source: https://tomesphere.com/paper/PMC12956373