Gidofalvi Gergely, Shepard Ron
Chemical Sciences and Engineering Division, Argonne National Laboratory, Argonne, Illinois 60439, USA.
J Comput Chem. 2009 Nov 30;30(15):2414-9. doi: 10.1002/jcc.21275.
Most electronic structure methods express the wavefunction as an expansion of N-electron basis functions that are chosen to be either Slater determinants or configuration state functions. Although the expansion coefficient of a single determinant may be readily computed from configuration state function coefficients for small wavefunction expansions, traditional algorithms are impractical for systems with a large number of electrons and spatial orbitals. In this work, we describe an efficient algorithm for the evaluation of a single determinant expansion coefficient for wavefunctions expanded as a linear combination of graphically contracted functions. Each graphically contracted function has significant multiconfigurational character and depends on a relatively small number of variational parameters called arc factors. Because the graphically contracted function approach expresses the configuration state function coefficients as products of arc factors, a determinant expansion coefficient may be computed recursively more efficiently than with traditional configuration interaction methods. Although the cost of computing determinant coefficients scales exponentially with the number of spatial orbitals for traditional methods, the algorithm presented here exploits two levels of recursion and scales polynomially with system size. Hence, as demonstrated through applications to systems with hundreds of electrons and orbitals, it may readily be applied to very large systems.
大多数电子结构方法将波函数表示为N电子基函数的展开式,这些基函数被选择为斯莱特行列式或组态态函数。虽然对于小波函数展开,单个行列式的展开系数可以很容易地从组态态函数系数计算得到,但传统算法对于具有大量电子和空间轨道的系统是不切实际的。在这项工作中,我们描述了一种高效算法,用于评估作为图形收缩函数线性组合展开的波函数的单个行列式展开系数。每个图形收缩函数都具有显著的多组态特征,并依赖于相对较少的称为弧因子的变分参数。由于图形收缩函数方法将组态态函数系数表示为弧因子的乘积,因此与传统组态相互作用方法相比,可以更有效地递归计算行列式展开系数。虽然传统方法计算行列式系数的成本随空间轨道数量呈指数增长,但本文提出的算法利用了两级递归,并且随系统大小呈多项式增长。因此,通过对具有数百个电子和轨道的系统的应用表明,它可以很容易地应用于非常大的系统。