论文标题
波函数ANSATZ（但周期性）网络和均质电子气体
Wave function Ansatz (but Periodic) Networks and the Homogeneous Electron Gas
论文作者
论文摘要
我们设计了一个神经网络ANSATZ，以在变异上找到同质电子气体的地面波函数，这是一种相互作用的fermions的扩展系统物理学中的基本模型。我们研究了7、14和19个电子的自旋和顺磁性相，在$ r_s = 1 $到$ r_s = 100 $的广泛范围内，即使在具有非常强大的纠正挑战的挑战性方面，也获得了相似或更高的精度。我们的工作将神经网络Ansätze的先前应用扩展到分子系统，并采用处理周期性边界条件的方法，并进行了两个显着的更改以提高性能：通过自旋对齐方式分配成对流并从网络中生成轨道的回流坐标。我们说明了高质量波函数在计算还原的单个粒子密度矩阵中的优势。该贡献将神经网络模型建立为周期性电子系统的灵活且高精度的Ansätze，这是朝着晶体固体应用的重要一步。
We design a neural network Ansatz for variationally finding the ground-state wave function of the Homogeneous Electron Gas, a fundamental model in the physics of extended systems of interacting fermions. We study the spin-polarised and paramagnetic phases with 7, 14 and 19 electrons over a broad range of densities from $r_s=1$ to $r_s=100$, obtaining similar or higher accuracy compared to a state-of-the-art iterative backflow baseline even in the challenging regime of very strong correlation. Our work extends previous applications of neural network Ansätze to molecular systems with methods for handling periodic boundary conditions, and makes two notable changes to improve performance: splitting the pairwise streams by spin alignment and generating backflow coordinates for the orbitals from the network. We illustrate the advantage of our high quality wave functions in computing the reduced single particle density matrix. This contribution establishes neural network models as flexible and high precision Ansätze for periodic electronic systems, an important step towards applications to crystalline solids.
