تقرير
Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators
| العنوان: | Accelerating Electron Dynamics Simulations through Machine Learned Time Propagators |
|---|---|
| المؤلفون: | Shah, Karan, Cangi, Attila |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Condensed Matter - Materials Science, Computer Science - Machine Learning, Physics - Computational Physics |
| الوصف: | 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. Comment: 9 pages, 5 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.09628 |
| رقم الأكسشن: | edsarx.2407.09628 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |