基于电子配对方法的自然轨道函数软最大化参数化
摘要
Within the framework of natural orbital functional theory, having a convenient representation of the occupation numbers and orbitals becomes critical for the computational performance of the calculations. Recognizing this, we propose an innovative parametrization of the occupation numbers that takes advantage of the electron-pairing approach used in Piris natural orbital functionals through the adoption of the softmax function, a pivotal component in modern deep-learning models. Our approach not only ensures adherence to the N-representability of the first-order reduced density matrix (1RDM) but also significantly enhances the computational efficiency of 1RDM functional theory calculations. The effectiveness of this alternative parameterization approach was assessed using the W4-17-MR molecular set, which demonstrated faster and more robust convergence compared to previous implementations.
引用
@article{arxiv.2403.09462,
title = {Deep-learning-assisted optical communication with discretized state space of structured light},
author = {Minyang Zhang and Dong-Xu Chen and Pengxiang Ruan and Jun Liu and Jun-Long Zhao and Chui-Ping Yang},
journal= {arXiv preprint arXiv:2403.09462},
year = {2024}
}
备注
10 pages, 7 figures