Ma Xiaoyao, Hall Randall W, Löffler Frank, Kowalski Karol, Bhaskaran-Nair Kiran, Jarrell Mark, Moreno Juana
Department of Physics and Astronomy, Louisiana State University, Baton Rouge, Louisiana 70803, USA.
Department of Natural Sciences and Mathematics, Dominican University of California, San Rafael, California 94901, USA.
J Chem Phys. 2016 Jan 7;144(1):014101. doi: 10.1063/1.4939145.
The Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is used to calculate the ab initio ground state energies for multiple geometries of the H2O, N2, and F2 molecules. The method is based on Feynman's path integral formulation of quantum mechanics and has two stages. The first stage is called the learning stage and reduces the well-known QMC minus sign problem by optimizing the linear combinations of Slater determinants which are used in the second stage, a conventional QMC simulation. The method is tested using different vector spaces and compared to the results of other quantum chemical methods and to exact diagonalization. Our findings demonstrate that the SiLK method is accurate and reduces or eliminates the minus sign problem.
基于符号学习扭结（SiLK）的量子蒙特卡罗（QMC）方法用于计算H₂O、N₂和F₂分子多种几何构型的从头算基态能量。该方法基于费曼的量子力学路径积分表述，有两个阶段。第一阶段称为学习阶段，通过优化在第二阶段（传统QMC模拟）中使用的斯莱特行列式的线性组合，减少了著名的QMC负号问题。该方法在不同向量空间上进行了测试，并与其他量子化学方法的结果以及精确对角化结果进行了比较。我们的研究结果表明，SiLK方法是准确的，并且减少或消除了负号问题。
