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使用基于神经网络的保利电位的自我一致的电子密度与外结构

Aparna Gangwar¹, Satya S Bulusu¹, Amit Kumar Das²

¹Department of Chemistry, Indian Institute of Technology Indore, Simrol, Indore 453552, India.

The Journal of chemical physics

|January 15, 2025

概括

本研究引入了一个神经网络 (NN) 方法,以准确地表示无轨密度函数理论 (OF-DFT) 中的保利潜力. 这种方法通过改进动能功能的近似来增强原子系统的电子结构计算.

科学领域:

计算物理计算物理
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:

无轨密度函数理论 (OF-DFT) 为电子结构计算提供了近线性缩放.
在OF-DFT中的一个关键挑战是准确地表示非相互作用的动能函数.
保利运动能函数及其相应的潜力仍然是重要的障碍.

研究的目的:

开发一个前神经网络 (NN) 模型来表示保利潜力.
通过提供强大的保利电位近似,提高OF-DFT计算的准确性.
为了使原子系统的量子力学计算更加有效.

主要方法:

一个前神经网络被训练来预测使用电子密度网格作为输入的保利电位.
基于NN的保利潜力与霍恩伯格-科恩变量原理相结合.
非相互作用的动能是通过结合NN衍生的保利动能和·泽克尔动能来计算的.

主要成果:

该NN方法成功地产生了具有精确原子外结构的自相一致的辐射密度.
在较小的原子中观察到高精度,在较大的原子中观察到一些偏差.
该方法有效地计算了保利电位和动能,而不需要函数导数.

结论:

神经网络提供了一个强大的工具,用于近似复杂的功能在OF-DFT.
这项工作在应用机器学习以提高OF-DFT准确性和效率方面取得了重大进展.
开发的方法为原子系统中改进的量子力学计算提供了实用途径.

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