调整机器学习密度函数的变化原理:非相互作用的动能函数
Pablo Del Mazo-Sevillano1,2, Jan Hermann2,3
1Departamento de Química Física Aplicada, Universidad Autónoma de Madrid, Módulo 14, 28049 Madrid, Spain.
The Journal of chemical physics
|November 16, 2023
概括
这项研究引入了一种新的深度神经网络方法来训练动能密度函数. 该方法在一维系统和原子系统中显示出出色的结果,推进密度函数理论 (DFT) 应用.
科学领域:
- 计算化学计算化学
- 量子力学就是量子力学.
- 机器学习 机器学习
背景情况:
- 密度函数理论 (DFT) 依赖于Kohn-Sham方法来计算非相互作用的动能.
- 准确的动能函数对于释放DFT的全部潜力至关重要,但由于其非局部性质,仍然具有挑战性.
- 与交换相关函数相比,对于动能函数的现有近似方法取得的成功有限.
研究的目的:
- 开发和测试一种新的,高效的规范化方法,用于使用深度神经网络训练密度函数.
- 专注于提高DFT内动能函数的精度.
- 为了证明拟议的机器学习方法对其他DFT函数的概括性,例如交换相关性.
主要方法:
- 实施一种新的规范化技术,用于训练深度神经网络.
- 该方法用于训练动能密度函数的应用.
- 在一维系统 (链,不相互作用的电子) 和原子系统 (前两个阶段) 上测试训练的函数.
主要成果:
- 拟议的规范化方法在训练测试系统的动能功能的方面取得了很好的表现.
- 通过使用相同的机器学习方法训练交换相关函数来证明成功的概括.
- 从机器学习的角度提供了对动能和交换相关函数的对比特征的见解.
结论:
- 基于深度神经网络的规范化方法为开发精确密度函数提供了一个高效和有前途的途径.
- 这种方法显示出有很大的潜力,可以提高DFT计算的能力,特别是动能计算.
- 该研究强调了机器学习技术在量子化学和材料科学中的应用性和适应性.
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