Partition of optical properties into orbital contributions

Literature Information

Publication Date 2019-06-20
DOI 10.1039/C9CP02662B
Impact Factor 3.676
Authors

Josep M. Luis


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Abstract

Nonlinear optical properties (NLOPs) play a major role in photonics, electro-optics and optoelectronics, and other fields of modern optics. The design of new NLO molecules and materials has benefited from the development of computational tools to analyze the relationship between the electronic structure of molecules and their optical response. In this paper, we present a new means to analyze the response property through the partition of NLOPs in terms of orbital contributions (PNOC). This tool can be used to obtain a real-space representation of the NLOPs, providing a powerful visualization aid to connect the magnitude of the optical property with some parts of the molecule. Unlike other methods to analyze NLOPs, the PNOC decomposes the optical property into orbitals of the unperturbed system, furnishing this method with the ability to assess the performance of single- and multi-determinant electronic structure methods. PNOC can be also used to design small basis sets for an accurate description of large systems, saving a substantial amount of computer time for the calculation of optical properties.

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DOI: 10.1039/B510291J

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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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