走向使用神经网络局部混合函数的双局部双混合函数.
Nóra Kovács1, Szymon Śmiga2, Martin Kaupp1
1Technische Universität Berlin, Institut für Chemie, Theoretische Chemie/Quantenchemie, Sekr. C7, Straße des 17. Juni 135, D-10623 Berlin, Germany.
Journal of chemical theory and computation
|February 13, 2026
概括
第一个双重局部双重混合 (DLDH) 函数优化了取决于位置的混合,以进行交换和相关联. 优化的DLDH表现出更好的性能,可能消除了量子化学计算中分散校正的需要.
科学领域:
- 量子化学 是一个量子化学.
- 计算化学计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 传统的双混合功能器使用固定的混合参数.
- 局部混合函数 (LMF) 提供位置依赖的参数化.
- 神经网络可以用来训练像LMFs这样的复杂功能.
研究的目的:
- 优化和评估新的"双局部双混合" (DLDH) 功能.
- 为了研究DLDHs与位置依赖交换和相关性混合的性能.
- 评估对DLDHs进行实证分散校正的必要性.
主要方法:
- 开发和培训基于神经网络的局部混合函数 (LMFs) 以进行交换和相关联.
- 使用W4-17原子化能量和BH76屏障高度对DLDH函数的评估.
- 与常量混合的局部双杂交和局部杂交相比较.
- 对-解离曲线的应用.
主要成果:
- DLDH功能实现了高性能,与现有方法相匹配或超过现有方法.
- 优化的DLDHs,特别是DL2DH变体,使得实证分散校正不必要.
- 在非对应相互作用区域中的大相关LMF值解释了性能的提高.
- 位置依赖混合提供了改善强相关性效应处理的潜力.
结论:
- DLDHs代表了密度函数理论的一个有希望的进步.
- 在DLDH中取决于位置的混合显著提高了准确性,并可以消除对分散的纠正的需要.
- 进一步开发DLDH可以导致更准确和更有效的量子化学计算.
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