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Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure.

Yuxing Zhou1, William Kirkpatrick1, Volker L Deringer1

  • 1Department of Chemistry, Inorganic Chemistry Laboratory, University of Oxford, Oxford, OX1 3QR, UK.

Advanced Materials (Deerfield Beach, Fla.)
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Summary

Machine learning simulations reveal amorphous phosphorus's atomic structure and behavior under pressure. Insights into its battery anode potential are gained by analyzing structural changes and cluster connectivity.

Keywords:
amorphous solidsmachine learningmaterials modelingmolecular dynamics

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Amorphous phosphorus (a-P) is of interest for its complex atomic structure.
  • It is increasingly studied as a potential anode material for advanced batteries.
  • Understanding a-P at the atomistic level presents significant challenges.

Purpose of the Study:

  • To investigate the atomic structure of amorphous phosphorus using advanced simulation techniques.
  • To explore how the structure of a-P responds to applied pressure.
  • To provide a computational framework for studying disordered functional materials.

Main Methods:

  • Large-scale molecular-dynamics simulations were employed.
  • A machine-learning-based interatomic potential was developed for phosphorus.
  • Analysis of structural features like rings, clusters, and voids under varying pressure.

Main Results:

  • The simulation model revealed abundant five-membered rings and complex seven- and eight-atom clusters in a-P.
  • Hysteresis in medium-range order recovery was observed during compression and decompression cycles.
  • Moderate pressures (up to 5 GPa) preserved cluster connectivity, while higher pressures disrupted it.

Conclusions:

  • Machine learning-driven molecular dynamics offers new insights into amorphous phosphorus structure and properties.
  • The study provides a foundation for further computational investigations of a-P.
  • This approach demonstrates the power of ML modeling in understanding disordered functional materials.