用深度学习学习全组态相互作用电子相关性
Learning Full Configuration Interaction Electron Correlations with Deep Learning
摘要：
在这份报告中，我们提出了一个名为电子相关电位神经网络（eCPNN）的深度学习框架，它可以学习简洁紧凑的电位函数。这些函数能有效地描述多电子原子中电子间复杂的瞬时空间关联。eCPNN以无监督的方式进行训练，其中有限的信息来自全组态相互作用（FCI）单电子密度函数，在预定义的准确度范围内。使用eCPNN产生的有效相关势函数，我们可以预测每个研究的原子系统的总能量与FCI能量相比，具有显着的准确性。
英文摘要：
In this report, we present a deep learning framework termed the Electron Correlation Potential Neural Network (eCPNN) that can learn succinct and compact potential functions. These functions can effectively describe the complex instantaneous spatial correlations among electrons in many--electron atoms. The eCPNN was trained in an unsupervised manner with limited information from Full Configuration Interaction (FCI) one--electron density functions within predefined limits of accuracy. Using the effective correlation potential functions generated by eCPNN, we can predict the total energies of each of the studied atomic systems with a remarkable accuracy when compared to FCI energies.
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