告知实证适配密度函数关于电子间相互作用物理学的信息
Timofey V Losev1,2, Ilya D Ivanov1,3, Igor S Gerasimov1
1N.D. Zelinsky Institute of Organic Chemistry of Russian Academy of Sciences, 119991 Moscow, Russian Federation.
The journal of physical chemistry. A
|December 6, 2024
概括
开发基于物理的函数可以提高计算化学的准确性和可靠性. 这种方法克服了纯配密度函数中常见的过拟合问题,增强了各种属性的预测能力.
科学领域:
- 计算化学的计算化学
- 量子化学 是一个量子化学.
- 材料科学 材料科学 材料科学
背景情况:
- 由于电子间相互作用的复杂性,构建精确的密度函数具有挑战性.
- 当前的装配技术往往导致过度装配,导致未经训练的属性不可靠的功能.
- 需要一种方法来确保密度函数的训练过程中的物理正确性,特别是在神经网络等高级模型中.
研究的目的:
- 为训练密度函数设计和应用基于物理的方法.
- 使用这种新方法重新参数化M06-2X功能,以提高其准确性和可靠性.
- 探索这种方法在开发未来基于神经网络的功能方面的潜力.
主要方法:
- 开发了一种新的基于物理的训练方法,以在功能发育过程中保持正确的身体行为.
- 应用了这种方法来对其原始训练数据集上的M06-2X函数进行重新参数化.
- 将新的基于物理学的函数 (piM06-2X,piM06-2X-DL) 与现有的函数 (如M06-2X和PBE0.0) 的性能进行比较.
主要成果:
- 基于物理的函数 piM06-2X 和 piM06-2X-DL 实现了与热化学任务的 M06-2X 相比的精度.
- 这些新的函数也达到PBE0的电子密度属性准确度,结合了两者的强度.
- 令人惊的是,PBE-2X功能在没有任何装配的情况下表现出类似的性能,这表明固有的物理接地.
结论:
- 基于物理学的方法成功地创建了更强大,更准确的密度函数.
- 这种方法提供了一种方法,可以结合重装和物理接地功能的好处.
- 拟议的技术对于未来开发基于神经网络的复杂量子化学函数是必不可少的.
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