使用MACE-MP-0机器学习原子间势建模二氧化硅
Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials
AI总结：
本研究应用MACE-MP-0机器学习原子间势建模硅质沸石骨架能量及高压下二氧化硅多晶型相变，结果与实验和DFT数据吻合，验证了该势函数的可靠性。
摘要：
二氧化硅多晶型物和沸石因其多样的结构特征、不同条件下的热力学和机械稳定性以及地质重要性，在众多工业应用中具有基础性地位。计算建模在理解二氧化硅及含硅酸盐（包括沸石）的结构与功能之间的关系方面发挥了关键作用。本研究中，我们应用MACE-MP-0机器学习原子间势（ML-IP）来建模硅质沸石的骨架能量，并检验高压条件下二氧化硅和ZSM-5多晶型物的相变。研究结果重现了硅质沸石相对于α-石英的已知亚稳态，MACE-MP-0中型ML-IP和密度泛函理论（DFT）方法计算出的微孔相与致密相之间的能量差与实验量热数据高度吻合。高压模拟揭示了二氧化硅的石英、柯石英和斯石英多晶型物存在明显不同的压缩行为，其中柯石英和斯石英在高压下稳定性提升，这与实验数据一致。计算得到的石英到柯石英（约3.5 GPa）以及柯石英到斯石英（约9 GPa）的相变压力与实验结果接近，证明了MACE ML-IP在建模二氧化硅多晶型物的结构和能量性质方面的可靠性。
英文摘要：
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 α-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.
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