多体施罗丁格方程的自旋对称强制解与深度神经网络
Zhe Li1, Zixiang Lu2,3, Ruichen Li2,4
1ByteDance Research, Fangheng Fashion Center, Beijing, P. R. China. lizhe.qc@bytedance.com.
Nature computational science
|December 5, 2024
概括
这项研究引入了一种新的深度神经网络变量蒙特卡罗 (VMC) 方法,以准确计算具有特定自旋对称性的量子状态. 这种方法有效地解决了相关系统中基态和兴奋态的施罗丁格方程.
科学领域:
- 量子化学 是一个量子化学.
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 解决施罗丁格方程对于理解分子行为至关重要.
- 传统的方法与强烈相关的系统和保持旋转对称性作斗争.
- 深度神经网络为增强量子力学计算提供了一个有希望的途径.
研究的目的:
- 开发一种计算效率高的方法来解决施罗丁格方程.
- 在基于神经网络的变量蒙特卡洛 (VMC) 计算中强制执行和维护旋转对称性.
- 准确预测具有强电子相关性系统中的基层和激发状态的能量.
主要方法:
- 深度神经网络与变量蒙特卡洛 (VMC) 方法的集成.
- 实施修改后的优化目标以强制执行旋转对称性.
- 用于计算具有指定自旋对称性的基态和兴奋状态的应用程序.
主要成果:
- 该方法准确地预测能量,同时保持正确的旋转对称性.
- 实现了解决施罗丁格方程的低计算成本.
- 在计算自旋间隙能量方面取得了成功,包括单点三点间隙在二极根中.
结论:
- 开发的框架为计算具有特定自旋对称性的量子态提供了强大的和高效的方法.
- 这种方法对于强烈相关的系统和旋转间隙计算特别有效.
- 深度学习与VMC的整合推动了量子化学和计算物理领域的发展.
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