Efficient Ab Initio Auxiliary-Field Quantum Monte Carlo Calculations in Gaussian Bases via Low-Rank Tensor Decomposition
Efficient Ab Initio Auxiliary-Field Quantum Monte Carlo Calculations in Gaussian Bases via Low-Rank Tensor Decomposition
复制标题
通过低阶张量分解在高斯基上进行高效从头算辅助场量子蒙特卡罗计算
DOI：
10.1021/acs.jctc.8b00996
复制
发表时间：
2019
影响因子：
5.5
通讯作者：
Chan, Garnet Kin-Lic
中科院分区：
文献类型：
--
作者：
Motta, Mario;Shee, James;Zhang, Shiwei;Chan, Garnet Kin-Lic
我们描述了一种算法，以减少成本的非相干场量子蒙特卡罗（AFQMC）计算的电子结构问题。该技术使用嵌套的电子排斥积分（ERI）的低秩因式分解。虽然传统的AFQMC计算高斯基地规模的成本，其中N是基础的大小，我们表明，基态能量可以通过张量分解计算，减少内存需求和次四次标度。该算法被应用到氢链和正方形网格，水团簇，和六边形BN。在所有情况下，我们观察到显着的内存节省，并为较大的系统，减少，次四次模拟时间。
We describe an algorithm to reduce the cost of auxiliary-field quantum Monte Carlo (AFQMC) calculations for the electronic structure problem. The technique uses a nested low-rank factorization of the electron repulsion integral (ERI). While the cost of conventional AFQMC calculations in Gaussian bases scales as, whereNis the size of the basis, we show that ground-state energies can be computed through tensor decomposition with reduced memory requirements and subquartic scaling. The algorithm is applied to hydrogen chains and square grids, water clusters, and hexagonal BN. In all cases, we observe significant memory savings and, for larger systems, reduced, subquartic simulation time.