Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909374528618496 |
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| author | Nasir, Jamal Abdul Guan, Jingcheng Jee, Woongkyu Woodley, Scott M. Sokol, Alexey A. Catlow, C. Richard A. Elena, Alin Marin |
| author_facet | Nasir, Jamal Abdul Guan, Jingcheng Jee, Woongkyu Woodley, Scott M. Sokol, Alexey A. Catlow, C. Richard A. Elena, Alin Marin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00436 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials Nasir, Jamal Abdul Guan, Jingcheng Jee, Woongkyu Woodley, Scott M. Sokol, Alexey A. Catlow, C. Richard A. Elena, Alin Marin Materials Science 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. |
| title | Modelling Silica using MACE-MP-0 Machine Learnt Interatomic Potentials |
| topic | Materials Science |
| url | https://arxiv.org/abs/2411.00436 |