2005
DOI: 10.1002/jcc.20350
|View full text |Cite
|
Sign up to set email alerts
|

Parallel coupled perturbed CASSCF equations and analytic CASSCF second derivatives

Abstract: A parallel algorithm for solving the coupled-perturbed MCSCF (CPMCSCF) equations and analytic nuclear second derivatives of CASSCF wave functions is presented. A parallel scheme for evaluating derivative integrals and their subsequent use in constructing other derivative quantities is described. The task of solving the CPMCSCF equations is approached using a parallelization scheme that partitions the electronic hessian matrix over all processors as opposed to simple partitioning of the 3 N solution vectors amo… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
28
0

Year Published

2006
2006
2021
2021

Publication Types

Select...
7
2

Relationship

1
8

Authors

Journals

citations
Cited by 30 publications
(28 citation statements)
references
References 49 publications
0
28
0
Order By: Relevance
“…Because analytic MCSCF Hessians are so computationally demanding, it is essential to implement them using a scalable algorithm. The scalability of the parallel MCSCF Hessian code in GAMESS [5] is illustrated in Figure 3, for the biologically important molecule 7-azaindole. This calculation employs a relatively modest basis set and a modest MCSCF active space.…”
Section: Casscf Hessiansmentioning
confidence: 99%
“…Because analytic MCSCF Hessians are so computationally demanding, it is essential to implement them using a scalable algorithm. The scalability of the parallel MCSCF Hessian code in GAMESS [5] is illustrated in Figure 3, for the biologically important molecule 7-azaindole. This calculation employs a relatively modest basis set and a modest MCSCF active space.…”
Section: Casscf Hessiansmentioning
confidence: 99%
“…[18][19][20][21] The linearscaling QM algorithms [22][23][24][25] have primarily been developed for energy and gradient evaluations; the efficiency of Hessian computations has also been improved. [26][27][28] Some fragment based approaches [29][30][31][32][33][34][35][36][37][38][39][40] feature analytic second derivatives. [41][42][43][44][45][46] The fragment molecular orbital (FMO) method [47][48][49][50][51] is a fragment-based approach.…”
Section: Introductionmentioning
confidence: 99%
“…The transformed MO integrals are used in constructing B α matrices, but this transformation can be avoided in principle. The present implementation relies on an existing one, 35 and this part may be improved or rewritten in the future. In the opposite case, when the number of active orbitals (N act ) is greater than 10 for instance, it is the evaluations of the terms involving 4-RDM that are the most time-consuming, since the computational cost of these terms formally scales as O(N 9 act ).…”
Section: Methodsmentioning
confidence: 99%