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Spin-adapted selected configuration interaction in a determinant basis
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abstract
Selected configuration interaction (SCI) methods, when complemented with a second-order perturbative correction, provide near full configuration interaction (FCI) quality energies with only a small fraction of the Slater determinants of the FCI space. However, a selection criterion based on determinants alone does not ensure a spin-pure wave function. In other words, such SCI wave functions are not eigenfunctions of the $S^2$ operator. In many situations (bond breaking, magnetic system, excited state, etc), having a spin-adapted wave function is essential for a quantitatively correct description of the system. Here, we propose an efficient algorithm which, given an arbitrary determinant space, generates all the missing Slater determinants allowing one to obtain spin-adapted wave functions while avoiding manipulations involving configuration state functions. For example, generating all the possible determinants with 6 spin-up and 6 spin-down electrons in 12 open shells takes 21 CPU cycles per generated Slater determinant. The selection is still done with individual determinants, and one can take advantage of the basis of configuration state functions in the diagonalization of the Hamiltonian to reduce significantly the memory footprint.
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Cited by 1 Pith paper
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Machine learning configuration interaction for ab initio potential energy curves
MLCI with the ANN used as a hash function, configuration state functions, and geometry-to-geometry wavefunction transfer gives near-FCI potential energy curves for N2 and CO more cheaply than stochastic Monte Carlo CI.
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