Analytic gradients, geometry optimization and excited state potential energy surfaces from the particle-particle random phase approximation

Literature Information

Publication Date 2014-11-12
DOI 10.1039/C4CP04109G
Impact Factor 3.676
Authors

Du Zhang, Degao Peng, Peng Zhang


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Abstract

The energy gradient for electronic excited states is of immense interest not only for spectroscopy but also for the theoretical study of photochemical reactions. We present the analytic excited state energy gradient of the particle-particle random phase approximation (pp-RPA). The analytic gradient formula is developed from an approach similar to that of time-dependent density-functional theory (TDDFT). The formula is verified for both the Hartree–Fock and (Generalized) Kohn–Sham reference states via comparison with finite difference results. The excited state potential energy surfaces and optimized geometries of some small molecules are investigated, yielding results of similar or better quality compared to adiabatic TDDFT. The singlet-to-triplet instability in TDDFT resulting in underestimated energies of the lowest triplet states is eliminated by pp-RPA. Charge transfer excitations and double excitations, which are challenging for most adiabatic TDDFT methods, can be reasonably well captured by pp-RPA. Within this framework, ground state potential energy surfaces of stretched single bonds can also be described well.

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Source Journal

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
Articles per Year: 3036

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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