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Modelling Silica using MACE-MP Machine Learnt Interatomic Potentials
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DOI：10.1039/D5CP01882J.png)
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Abstract
En 中文
Silica polymorphs and zeolites are fundamental to a wide range of industrial applications owing to their diverse structural characteristics and thermodynamic and mechanical stability under varying conditions. Computational modelling has played a crucial role in understanding the relationship between the structure and functionality of silicas and silicates; including zeolites. In this study; we apply the MACE machine learnt interatomic potentials (MACE MP) to model the framework energies of siliceous zeolites and examine the phase transitions of silica and ZSM-5 polymorphs under high-pressure conditions. MACE MP offers versatility by handling silicas with different coordination numbers; unlike earlier IPs such as Sanders potentials (J. Chem. Soc.; Chem. Commun. 1984; 19; 1271-1273); which are typically restricted to four-coordinated Si environments and demand extensive re-parameterisation for higher coordination systems. The results reproduce the known metastability of siliceous zeolites relative to α-quartz; with energy differences between microporous and dense phases calculated by MACE-MP-0 medium and density functional theory (DFT) methods closely aligning with experimental calorimetric data. The high-pressure simulations reveal distinct compression behaviour in the quartz; coesite; and stishovite polymorphs of silica; with coesite and stishovite showing increased stability at elevated pressures in line with experimental data. The calculated phase transition pressures from quartz to coesite (~3.5 GPa) and coesite to stishovite (~9 GPa) are close to experimental findings; demonstrating the reliability of MACE-mp0 in modelling the structural and energetic properties of silica polymorphs. Furthermore; we examine the behaviour of fluoride ions in zeolite cages using MACE-MP; capturing known structural motifs such as pentacoordinated [SiO₄F]⁻ units and central cage-bound F⁻ species; in agreement with prior DFT and experimental observations. Thus; we assess and demonstrate the suitability of off-the-shelf machine-learned foundation models; based on MACE-MP framework; for modelling silica and silicates; materials of high importance from earth sciences to electronics and catalysis.
Keywords:
silica polymorphs
zeolites
machine learning
interatomic potentials
high-pressure phase transitions
Journal
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2.9
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4.3K
Citations:
12.4W
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