Ionic Liquid Molecular Dynamics Simulation with Machine Learning Force Fields: DPMD and MACE
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| Format: | Preprint |
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2025
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| _version_ | 1866912290013446144 |
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| author | Park, Anseong Ryu, Jaeyune Lee, Won Bo |
| author_facet | Park, Anseong Ryu, Jaeyune Lee, Won Bo |
| contents | Machine learning force fields (MLFFs) are gaining attention as an alternative to classical force fields (FFs) by using deep learning models trained on density functional theory (DFT) data to improve interatomic potential accuracy. In this study, we develop and apply MLFFs for ionic liquids (ILs), specifically PYR14BF4 and LiTFSI/PYR14TFSI, using two different MLFF frameworks: DeePMD (DPMD) and MACE. We find that high-quality training datasets are crucial, especially including both equilibrated (EQ) and non-equilibrated (nEQ) structures, to build reliable MLFFs. Both DPMD and MACE MLFFs show good accuracy in force and energy predictions, but MACE performs better in predicting IL density and diffusion. We also analyze molecular configurations from our trained MACE MLFF and notice differences compared to pre-trained MACE models like MPA-0 and OMAT-0. Our results suggest that careful dataset preparation and fine-tuning are necessary to obtain reliable MLFF-based MD simulations for ILs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_18249 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ionic Liquid Molecular Dynamics Simulation with Machine Learning Force Fields: DPMD and MACE Park, Anseong Ryu, Jaeyune Lee, Won Bo Chemical Physics Machine learning force fields (MLFFs) are gaining attention as an alternative to classical force fields (FFs) by using deep learning models trained on density functional theory (DFT) data to improve interatomic potential accuracy. In this study, we develop and apply MLFFs for ionic liquids (ILs), specifically PYR14BF4 and LiTFSI/PYR14TFSI, using two different MLFF frameworks: DeePMD (DPMD) and MACE. We find that high-quality training datasets are crucial, especially including both equilibrated (EQ) and non-equilibrated (nEQ) structures, to build reliable MLFFs. Both DPMD and MACE MLFFs show good accuracy in force and energy predictions, but MACE performs better in predicting IL density and diffusion. We also analyze molecular configurations from our trained MACE MLFF and notice differences compared to pre-trained MACE models like MPA-0 and OMAT-0. Our results suggest that careful dataset preparation and fine-tuning are necessary to obtain reliable MLFF-based MD simulations for ILs. |
| title | Ionic Liquid Molecular Dynamics Simulation with Machine Learning Force Fields: DPMD and MACE |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2503.18249 |