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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
Summary
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.
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.
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