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      Quantification of the Helicality of Helical Molecular Orbitals

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          Abstract

          The frontier molecular orbital (MO) topology of linear carbon molecules, such as polyynes, can be visually identified as helices. However, there is no clear way to quantify the helical curvature of these π-MOs, and it is thus challenging to quantify correlations between the helical curvature and molecular properties. In this paper, we develop a method that enables us to compute the helical curvature of MOs based on their nodal planes. Using this method, we define a robust way of quantifying the helical nature of MOs (helicality) by their deviation from a perfect helix. We explore several limiting cases, including polyynes, metallacumulenes, cyclic allenes, and spiroconjugated systems, where the change in helical curvature is subtle yet clearly highlighted with this method. For example, we show that strain only has a minor effect on the helicality of the frontier orbitals of cycloallenes and that the MOs of spiroconjugated systems are close to perfect helices around the spiro-carbon. Our work provides a well-defined method for assessing orbital helicality beyond visual inspection of MO isosurfaces, thus paving the way for future studies of how the helicality of π-MOs affects molecular properties.

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          Generalized Gradient Approximation Made Simple

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            Array programming with NumPy

            Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves 1 and in the first imaging of a black hole 2 . Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
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              Gaussian basis sets for use in correlated molecular calculations. III. The atoms aluminum through argon

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                Author and article information

                Journal
                J Phys Chem A
                J Phys Chem A
                jx
                jpcafh
                The Journal of Physical Chemistry. a
                American Chemical Society
                1089-5639
                1520-5215
                07 September 2021
                16 September 2021
                : 125
                : 36
                : 8107-8115
                Affiliations
                Department of Chemistry and Nano-Science Center, University of Copenhagen , Universitetsparken 5, DK-2100 Copenhagen Ø, Denmark
                Author notes
                Author information
                https://orcid.org/0000-0001-8171-6374
                https://orcid.org/0000-0002-7270-8353
                https://orcid.org/0000-0002-2018-1529
                Article
                10.1021/acs.jpca.1c05799
                8450904
                34491758
                cbafc91a-a70b-496b-9630-0de445a5fc39
                © 2021 The Authors. Published by American Chemical Society

                Permits non-commercial access and re-use, provided that author attribution and integrity are maintained; but does not permit creation of adaptations or other derivative works ( https://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 30 June 2021
                : 23 August 2021
                Funding
                Funded by: H2020 European Research Council, doi 10.13039/100010663;
                Award ID: 865870
                Categories
                Article
                Custom metadata
                jp1c05799
                jp1c05799

                Physical chemistry
                Physical chemistry

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