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Modelling silica using MACE-MP machine learnt interatomic potentials.

Jamal Abdul Nasir1, Jingcheng Guan1, Woongkyu Jee1

  • 1Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ, UK. c.r.a.catlow@ucl.ac.uk.

Physical Chemistry Chemical Physics : PCCP
|September 2, 2025
PubMed
Summary
This summary is machine-generated.

Machine-learned potentials accurately model silica polymorphs and zeolites, predicting phase transitions and fluoride ion behavior. This demonstrates the effectiveness of MACE machine-learned potentials for diverse silica material simulations.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Mineral Physics

Background:

  • Silica polymorphs and zeolites are crucial in mineralogy and industry due to their structural diversity and stability.
  • Computational modeling is vital for understanding the structure-function relationships in silicas and silicates.
  • Existing interatomic potentials (IPs) have limitations in handling varying silicon coordination numbers.

Purpose of the Study:

  • To apply MACE machine-learned potentials (MACE MP) for modeling siliceous zeolites and silica polymorph phase transitions.
  • To assess the versatility of MACE MP in handling different silicon coordination states.
  • To validate MACE MP's accuracy against experimental and density functional theory (DFT) data.

Main Methods:

  • Utilized MACE machine-learned interatomic potentials (MACE MP) for simulations.
  • Modeled framework energies of siliceous zeolites.
  • Simulated high-pressure phase transitions of silica polymorphs (quartz, coesite, stishovite).
  • Investigated fluoride ion behavior within zeolite cages.

Main Results:

  • MACE MP accurately reproduced the metastability of siliceous zeolites relative to α-quartz.
  • Calculated energy differences closely matched experimental calorimetric data.
  • High-pressure simulations revealed distinct compression behaviors for quartz, coesite, and stishovite.
  • Predicted phase transition pressures for quartz-coesite and coesite-stishovite align well with experimental values.
  • MACE MP successfully captured fluoride ion interactions in zeolites, including pentacoordinated units.

Conclusions:

  • MACE MP is a reliable tool for modeling structural and energetic properties of silica polymorphs.
  • The study validates the suitability of off-the-shelf machine-learned foundation models for silica materials.
  • MACE MP demonstrates broad applicability in earth sciences, electronics, and catalysis.