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从精确的交换相关潜力和能量中学习局部和半局部密度函数

Bikash Kanungo¹, Jeffrey Hatch², Paul M Zimmerman²

¹Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.

Science advances

|September 19, 2025

概括

研究人员开发了一种基于数据的方法,使用神经网络来创建密度函数理论 (DFT) 的准确交换相关 (XC) 函数. 这种方法显著提高了总能量和密度,为未来的功能发展提供了有前途的途径.

科学领域:

计算化学计算化学
材料科学材料科学材料科学
量子力学就是量子力学.

背景情况:

精确的交换关联 (XC) 函数对于密度函数理论 (DFT) 至关重要,但仍然是一个重大挑战.
尽管经过数十年的发展,现有的功能分子仍在努力实现通用化学准确性.

研究的目的:

提出一种新的数据驱动的学习XC函数的途径,使用精确的密度,能量和潜力.
为了证明基于神经网络 (NN) 的 XC 函数的有效性.

主要方法:

从精确的配置相互作用 (CI) 计算中获得精确的密度.
在CI密度上通过反向DFT推导出精确的XC能量和潜力.
训练了简单的基于NN的局部密度近似 (LDA) 和通用梯度近似 (GGA) 函数.

主要成果:

基于NN的LDA和GGA函数显示了总能量和密度的显著改善,即使在有限的数据上进行训练.
基于NN的GGA功能实现了与热化学数据集上的SCAN元GGA可比的准确性.

结论:

在XC函数的建模中使用XC潜能是有希望的.
这种数据驱动的方法可以为系统地为DFT开发更准确的XC函数铺平道路.

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