Quantum mechanics

Algorithm

Residual

Applied mathematics

Convergence (economics)

Selection (genetic algorithm)

Economics

Computer science

Mathematical optimization

Solver

Statistical physics

Configuration space

Physics

Mathematics

Artificial intelligence

Wave function

Economic growth

Journal of Chemical Theory and Computation

Even when starting with very poor initial guess, the iterative configuration interaction (iCI) approach [J. Chem. Theory Comput.12, 1169 (2016)] for strongly correlated electrons can converge from above to full CI (FCI) very quickly by constructing and diagonalizing a very small Hamiltonian matrix at each macro/micro-iteration. However, as a direct solver of the FCI problem, iCI is computationally very expensive. The problem can be mitigated by observing that a vast number of configurations have little weights in the wave function and hence do not contribute discernibly to the correlation energy. The real questions are as follows: (a) how to identify those important configurations as early as possible in the calculation and (b) how to account for the residual contributions of those unimportant configurations. It is generally true that if a high-quality yet compact variational space can be determined for describing static correlation, a low-order treatment of the residual dynamic correlation would then be sufficient. While this is common to all selected CI schemes, the "iCI with selection" scheme presented here has the following distinctive features: (1) the full spin symmetry is always maintained by taking configuration state functions (CSF) as the many-electron basis. (2) Although the selection is performed on individual CSFs, it is orbital configurations (oCFGs) that are used as the organizing units. (3) Given a coefficient pruning-threshold Cmin (which determines the size of the variational space for static correlation), the selection of important oCFGs/CSFs is performed iteratively until convergence. (4) At each iteration, for the growth of the wave function, the first-order interacting space is decomposed into disjoint subspaces so as to reduce memory requirement on the one hand and facilitate parallelization on the other hand. (5) Upper bounds (which involve only two-electron integrals) for the interactions between doubly connected oCFG pairs are used to screen each first-order interacting subspace before the first-order coefficients of individual CSFs are evaluated. (6) Upon convergence of the static correlation for a given Cmin, dynamic correlation is estimated using the state-specific Epstein-Nesbet second-order perturbation theory (PT2). The efficacy of the iCIPT2 scheme is demonstrated numerically using benchmark examples, including C2, O2, Cr2, and C6H6.

Iterative Configuration Interaction with Selection