基于神经网络的选择性配置交互方法对分子电子结构的选择性配置交互方法
Yorick L A Schmerwitz1,2, Louis Thirion1,3, Gianluca Levi1
1Science Institute and Faculty of Physical Sciences, University of Iceland, Reykjavík 107, Iceland.
Journal of chemical theory and computation
|February 27, 2025
概括
这项研究引入了一种新的计算方法,即选择性神经网络配置交互 (NNCI),用于准确的量子化学计算. NNCI显著减少了所需的决定因素的数量,使复杂的分子模拟更加有效.
科学领域:
- 量子化学 是一个量子化学.
- 计算物理 计算物理
- 材料科学 材料科学 材料科学
背景情况:
- 准确的量子化学计算对于理解分子性质至关重要.
- 传统的配置交互 (CI) 方法在大型系统的计算成本方面存在困难.
- 神经网络潜力为加速量子模拟提供了一个有希望的途径.
研究的目的:
- 开发和实施一种新的选择性神经网络配置交互 (NNCI) 方法.
- 评估NNCI对分子计算的性能.
- 为了证明NNCI在更大,更广泛的系统中的效率和适用性.
主要方法:
- 结合了Hartree-Fock与神经网络支持的量子集群解决器.
- 实现了选择性神经网络配置交互 (NNCI),具有灵活的基础集和边界条件.
- 对小分子的NNCI性能进行评估,将结果与完整的CI计算进行比较.
主要成果:
- 与全CI (10^10) 相比,NNCI准确地复制了N2的相关性能量,使用的决定因素显著减少 (4x10^5).
- 增加包含轨道的数量显示出明显的优势,而不是用更少的轨道接近完全的CI.
- 该方法证明了分子计算的高效率和可扩展性.
结论:
- 在电子结构计算方面,NNCI提供了一种计算效率高,准确的方法.
- 该方法在凝聚物质模拟软件中的实施扩大了CI计算的范围.
- NNCI适用于更广泛的问题,包括通过嵌入扩展系统.
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