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基于零级正规近似的相对论费米奥尼神经网络:ZORANet
Mengsa Wang1,2, Yuzhi Zhou2,3, Han Wang4,5
1Graduate School of China Academy of Engineering Physics, Beijing 100088, China.
Journal of chemical theory and computation
|January 26, 2026
概括
一个新的费米子神经网络ZORANet,使准确的相对论量子化学计算成为可能. 它改进了电子结构的现有方法,有效地处理电子相关性和相对论效应.
科学领域:
- 计算化学是一种计算化学.
- 量子力学就是量子力学.
- 科学中的人工智能.
背景情况:
- 相对论效应对于精确的电子结构计算至关重要,特别是对于重元素.
- 现有的深度神经网络方法在处理相对论量子化学问题时面临挑战.
- 标量零次正规近似 (ZORA) 是一种常见的相对论近似,但存在尺度依赖的误差.
研究的目的:
- 介绍ZORANet,一个新的费米子神经网络框架,用于使用标量ZORA进行相对论电子结构计算.
- 解决相对论计算中的数值挑战,包括电子核尖端校正和重要性采样.
- 开发变体 (缩放ZORANet,原子ZORANet) 以减轻ZORA的尺寸依赖的错误.
主要方法:
- 开发了ZORANet,这是一个基于标量ZORA的费米奥尼克神经网络框架.
- 实施了电子核尖端校正和重要性采样,以提高训练稳定性和减少错误.
- 创建了缩放的 ZORANet 和原子 ZORANet 变体,以解决尺寸依赖.
主要成果:
- ZORANet和缩放的ZORANet能量与类系统的确切ZORA和迪拉克结果有很好的一致性.
- 佐兰网 (ZORANet) 实现了第一和第二排元素的电离电位和电子亲和度的化学精度,性能优于费米网 (FermiNet).
- 原子ZORANet与化物解离能量的最新精度相匹配,超过了FermiNet.
结论:
- ZORANet是一种高精度的全电子方法,用于相对论量子化学.
- 它有效地处理电子相关性和相对论效应,计算成本与FermiNet.Net相比.
- ZORANet显示了开发完整的初始相对论量子化学方法的前景.
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