Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916336687382528 |
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| author | Shah, Karan Cangi, Attila |
| author_facet | Shah, Karan Cangi, Attila |
| contents | Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under various external perturbations such as laser fields. In this work, we present a novel approach to accelerate real time TDDFT based electron dynamics simulations using autoregressive neural operators as time-propagators for the electron density. By leveraging physics-informed constraints and high-resolution training data, our model achieves superior accuracy and computational speed compared to traditional numerical solvers. We demonstrate the effectiveness of our model on a class of one-dimensional diatomic molecules. This method has potential in enabling real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_09628 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators Shah, Karan Cangi, Attila Materials Science Machine Learning Computational Physics Time-dependent density functional theory (TDDFT) is a widely used method to investigate electron dynamics under various external perturbations such as laser fields. In this work, we present a novel approach to accelerate real time TDDFT based electron dynamics simulations using autoregressive neural operators as time-propagators for the electron density. By leveraging physics-informed constraints and high-resolution training data, our model achieves superior accuracy and computational speed compared to traditional numerical solvers. We demonstrate the effectiveness of our model on a class of one-dimensional diatomic molecules. This method has potential in enabling real-time, on-the-fly modeling of laser-irradiated molecules and materials with varying experimental parameters. |
| title | Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators |
| topic | Materials Science Machine Learning Computational Physics |
| url | https://arxiv.org/abs/2407.09628 |