基于神经网络的波函数的随时演变的随机表示
Bizi Huang1, Weizhong Fu1, Ji Chen1,2,3
1School of Physics, Peking University, Beijing 100871, People's Republic of China.
The Journal of chemical physics
|December 24, 2025
概括
本研究引入了一种新的计算方法,将随机表示和神经网络结合起来,以解决电子动态的时间依赖施罗丁格方程 (TDSE). 这种方法准确地模拟了激光场中的电离过程.
科学领域:
- 量子力学就是量子力学.
- 计算物理学的计算物理.
- 一秒钟的物理学.
背景情况:
- 解决时间依赖的施罗丁格方程 (TDSE) 对于理解超快光谱和激光物质相互作用中的电子动态至关重要.
- 精确的TDSE解决方案在计算上昂贵,因为希尔伯特空间与系统维度的指数增长.
研究的目的:
- 开发和验证一种计算效率高的方法来解决TDSE.
- 为了建模非电电子动力学,特别是强激光场下的电离过程.
主要方法:
- 将随机表示框架与神经网络波函数替代品集成.
- 在模拟电离动力学的一维单电子系统上进行验证.
- 探索扩展到三维系统的探索.
主要成果:
- 量子演变的准确复制,包括电离过程中的能量和双极演变.
- 证明了将方法应用于三维系统的可行性.
- 确定了对更高维度模拟的先进稳定策略的需求.
结论:
- 拟议的混合方法为模拟复杂的量子动态提供了一个有希望的途径.
- 该方法显示了在现实系统中准确建模超快电子动态的潜力.
- 需要进一步开发,以便对更高维度的问题有可靠的应用.
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