论文标题
通过接近基于原子的电子种群的函数，将电子信息纳入机器学习势能表面
Incorporating electronic information into Machine Learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations
论文作者
论文摘要
机器学习（ML）对密度功能理论（DFT）势能表面（PESS）的近似值表现出很大的希望，可以降低准确分子模拟的计算成本，但目前它们不适用于变化的电子状态，尤其是对当地电子结构对远程环境敏感的分子系统，它们并不适用于中等范围的中等环境。以此问题为焦点，我们提出了一种称为BPOPNN的新机器学习方法，用于获得对DFT PESS的有效近似值。该方法是基于接近真实的DFT能量作为原子上电子种群的函数，该函数可能会在受约束的DFT（CDFT）中实现。新方法通过深层神经网络与此功能创造了近似值。这些近似值因此将电子信息自然地纳入了ML方法，并优化模型能量相对于种群，就像DFT一样，电子术语可以自愿适应环境。我们通过对Li $ _n $ h $ _n $簇的各种计算确认了这种方法的有效性。
Machine Learning (ML) approximations to Density Functional Theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new Machine Learning approach called bpopNN for obtaining efficient approximations to DFT PESs. The methodology is based on approaching the true DFT energy as a function of electron populations on atoms, which may be realized in practice with constrained DFT (CDFT). The new approach creates approximations to this function with deep neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on Li$_n$H$_n$ clusters.
