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神经网络波函数的决定性方法的表达性
Ni Zhan1, William A Wheeler2, Gil Goldshlager3
1Department of Computer Science, Princeton University, Princeton, New Jersey 08544, United States.
Journal of chemical theory and computation
|September 17, 2025
概括
神经网络波函数为量子问题提供了准确的解决方案. 本研究揭示了不同波函数类型之间的边界,用于自旋独立问题,澄清了它们对自旋依赖场景的适用性.
科学领域:
- 量子力学就是量子力学.
- 计算化学是一种计算化学.
- 科学中的人工智能.
背景情况:
- 神经网络波函数越来越多地用于多体量子问题.
- 常见的方法涉及对抗对称的决定因素或决定因素的总和.
- 投射到固定旋转状态是典型的,但仅限于旋转独立的运算符.
研究的目的:
- 为了研究量子问题的不同波函数类型之间的关系.
- 为了确定这些波函数对自旋依赖的哈密尔顿函数的适用性.
- 在各种波函数表示之间建立严格的上限.
主要方法:
- 开发和分析神经网络的波函数形式主义.
- 研究Hartree-Fock-like决定因子,全自旋波函数和全决定因子波函数的特性.
- 在H3分子和2D均质电子气体上进行数值实验.
主要成果:
- 对于自旋独立的哈密尔顿数来说,一个严格的上限属性被证明.
- 旋转子和完全决定波函数之间的关系是通过投影来澄清的.
- 由于缺乏反对称性,完全决定性的波函数被证明无法适用于自旋依赖的哈密尔顿函数.
结论:
- 建立的界限为理解神经网络波函数行为提供了一个理论框架.
- 这些发现突出了对自旋依赖量子系统的固定自旋预测的局限性.
- 数字验证证实了分子和凝聚物质系统的理论界限.
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