Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials

TL;DR

This study demonstrates that MACE-MP-0 machine learned interatomic potentials accurately model silica polymorphs and zeolites, reproducing phase stability, transition pressures, and structural behaviors consistent with experimental data.

Contribution

The paper introduces the application of MACE-MP-0 ML-IP to model silica and zeolite structures, achieving results comparable to DFT and experiments for phase stability and transitions.

Findings

Reproduces metastability of siliceous zeolites relative to quartz

Accurately predicts phase transition pressures (~3.5 GPa and ~9 GPa)

Shows increased stability of coesite and stishovite at high pressures

Abstract

Silica polymorphs and zeolites are fundamental to a wide range of industrial applications owing to their diverse structural characteristics, thermodynamic and mechanical stability under varying conditions and due to their geological importance. 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-MP-0 machine learnt interatomic potentials (ML-IP) to model the framework energies of siliceous zeolites and examine the phase transitions of silica and ZSM-5 polymorphs under high-pressure conditions. The results reproduce the known metastability of siliceous zeolites relative to {\alpha}-quartz, with energy differences between microporous and dense phases calculated by MACE-MP-0 medium ML-IP 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 ML-IP in modelling the structural and energetic properties of silica polymorphs.