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Gustavo E. Scuseria

Researcher at Rice University

Publications -  666
Citations -  108911

Gustavo E. Scuseria is an academic researcher from Rice University. The author has contributed to research in topics: Density functional theory & Coupled cluster. The author has an hindex of 120, co-authored 658 publications receiving 95195 citations. Previous affiliations of Gustavo E. Scuseria include Budapest University of Technology and Economics & Ohio State University.

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Linear-scaling density-functional theory with Gaussian orbitals and periodic boundary conditions: Efficient evaluation of energy and forces via the fast multipole method

TL;DR: In this article, the Kohn-Sham density-functional method with Gaussian orbitals was used for the Coulomb problem with periodic boundary conditions, which achieves linear scaling of computational time with system size but also very high accuracy in all infinite summations.
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The closed‐shell coupled cluster single and double excitation (CCSD) model for the description of electron correlation. A comparison with configuration interaction (CISD) results

TL;DR: A single and double excitation coupled cluster (CCSD) method restricted to closed-shell single configuration reference functions is described in explicit detail in this article, where significant simplifications resulting from the restriction to closedshell systems are exploited to achieve maximum computational efficiency.
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The open-shell restricted Hartree—Fock singles and doubles coupled-cluster method including triple excitations CCSD (T): application to C+3

TL;DR: An open-shell restricted Hartree-Fock singles and doubles coupled-cluster method including perturbative triple excitations, CCSD (T), that does not include any spin contamination for the correlation energies, is computationally formulated and implemented in this paper.
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Linear scaling coupled cluster and perturbation theories in the atomic orbital basis

TL;DR: In this article, a reformulation of the coupled cluster equations in the atomic orbital (AO) basis is presented, which leads to a linear scaling algorithm for large molecules with respect to molecular size.