A general spin-complete spin-flip configuration interaction method

Literature Information

Publication Date 2018-01-10
DOI 10.1039/C7CP06837A
Impact Factor 3.676
Authors

Joani Mato, Mark S. Gordon


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Abstract

A new, general spin-correct spin-flip configuration interaction (SF-CI) method is introduced by extending the occupation restricted multiple active spaces (ORMAS) CI method in GAMESS. SF-ORMAS is a single reference CI method that utilizes a high-spin restricted open shell determinant on which an arbitrary amount of spin-flipped excitations are carried out to generate a wave function of desired multiplicity. Furthermore, the SF-ORMAS method allows for a flexible design of the active space(s) to fit the chemical problem at hand. Therefore, a variety of spin-flip schemes can be implemented within this one formalism. As SF-ORMAS mostly accounts for static correlation, dynamic correlation is included through perturbation theory. The new method is demonstrated for single and multiple bond breaking, diradical systems, vertical excitations of linear alkenes, and the singlet–triplet energy gap of silicon trimer.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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