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We present an interpretation of Fermi‐orbital descriptors (FODs) and argue that these descriptors carry chemical bonding information. We show that a bond order derived from these FODs agrees well with reference values, and highlight that optimized FOD positions used within the Fermi‐Löwdin orbital self‐interaction correction (FLO‐SIC) method correspond to expectations from Linnett's double‐quartet theory, which is an extension of Lewis theory. This observation is independent of the underlying exchange‐correlation functional, which is shown using the local spin density approximation, the Perdew–Burke–Ernzerhof generalized gradient approximation (GGA), and the strongly constrained and appropriately normed meta‐GGA. To make FOD positions generally accessible, we propose and discuss four independent methods for the generation of Fermi‐orbital descriptors, their implementation as well as their advantages and drawbacks. In particular, we introduce a re‐implementation of the electron force field, an approach based on the centers of mass of orbital densities, a Monte Carlo‐based algorithm, and a method based on Lewis‐like bonding information. All results are summarized with respect to future developments of FLO‐SIC and related methods. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

We present pyflosic, an open-source, general-purpose python implementation of the Fermi–Löwdin orbital self-interaction correction (FLO-SIC), which is based on the python simulation of chemistry framework (pyscf) electronic structure and quantum chemistry code. Thanks to pyscf, pyflosic can be used with any kind of Gaussian-type basis set, various kinds of radial and angular quadrature grids, and all exchange-correlation functionals within the local density approximation, generalized-gradient approximation (GGA), and meta-GGA provided in the libxc and xcfun libraries. A central aspect of FLO-SIC is the Fermi-orbital descriptors, which are used to estimate the self-interaction correction. Importantly, they can be initialized automatically within pyflosic; they can also be optimized within pyflosic with an interface to the atomic simulation environment, a python library that provides a variety of powerful gradient-based algorithms for geometry optimization. Although pyflosic has already facilitated applications of FLO-SIC to chemical studies, it offers an excellent starting point for further developments in FLO-SIC approaches, thanks to its use of a high-level programming language and pronounced modularity.

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