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Performance of a one-parameter correlation factor for transcorrelation: Study on a series of second row atomic and molecular systems

Werner Dobrautz, Aron J. Cohen, Ali Alavi, Emmanuel Giner

2022The Journal of Chemical Physics15 citationsDOIOpen Access PDF

Abstract

In this work, we investigate the performance of a recently proposed transcorrelated (TC) approach based on a single-parameter correlation factor [E. Giner, J. Chem. Phys. 154, 084119 (2021)] for systems involving more than two electrons. The benefit of such an approach relies on its simplicity as efficient numerical–analytical schemes can be set up to compute the two- and three-body integrals occurring in the effective TC Hamiltonian. To obtain accurate ground state energies within a given basis set, the present TC scheme is coupled to the recently proposed TC–full configuration interaction quantum Monte Carlo method [Cohen et al., J. Chem. Phys. 151, 061101 (2019)]. We report ground state total energies on the Li–Ne series, together with their first cations, computed with increasingly large basis sets and compare to more elaborate correlation factors involving electron–electron–nucleus coordinates. Numerical results on the Li–Ne ionization potentials show that the use of the single-parameter correlation factor brings on average only a slightly lower accuracy (1.2 mH) in a triple-zeta quality basis set with respect to a more sophisticated correlation factor. However, already using a quadruple-zeta quality basis set yields results within chemical accuracy to complete basis set limit results when using this novel single-parameter correlation factor. Calculations on the H2O, CH2, and FH molecules show that a similar precision can be obtained within a triple-zeta quality basis set for the atomization energies of molecular systems.

Topics & Concepts

Basis setBasis (linear algebra)Ground stateHamiltonian (control theory)Series (stratigraphy)Electronic correlationStatistical physicsMonte Carlo methodIonizationPhysicsComputationQuantum mechanicsElectronMathematicsAlgorithmMoleculeIonStatisticsGeometryMathematical optimizationBiologyPaleontologyAdvanced Chemical Physics StudiesSpectroscopy and Quantum Chemical StudiesMachine Learning in Materials Science
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