F. Heidar-Zadeh

GBasis: A Python library for evaluating functions, functionals, and integrals expressed with Gaussian basis functions

T.D. Kim, L. Pujal, M. Richer, M. Van Zyl, M. Martinez-Gonzales, A. Tehrani, V. Chuiko, G. Sánchez-Díaz, W. Sanchez, W. Adams, X. Huang, B.D. Kelly, E. Vohringer-Martinez, T. Verstraelen, F. Heidar-Zadeh, P.W. Ayers
The Journal of Chemical Physics
161(4), 042503
2024
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Abstract 

GBasis is a free and open-source Python library for molecular property computations based on Gaussian basis functions in quantum chemistry. Specifically, GBasis allows one to evaluate functions expanded in Gaussian basis functions (including molecular orbitals, electron density, and reduced density matrices) and to compute functionals of Gaussian basis functions (overlap integrals, one-electron integrals, and two-electron integrals). Unique features of GBasis include supporting evaluation and analytical integration of arbitrary-order derivatives of the density (matrices), computation of a broad range of (screened) Coulomb interactions, and evaluation of overlap integrals of arbitrary numbers of Gaussians in arbitrarily high dimensions. For circumstances where the flexibility of GBasis is less important than high performance, a seamless Python interface to the Libcint C package is provided. GBasis is designed to be easy to use, maintain, and extend following many standards of sustainable software development, including code-quality assurance through continuous integration protocols, extensive testing, comprehensive documentation, up-to-date package management, and continuous delivery. This article marks the official release of the GBasis library, outlining its features, examples, and development.

The Tale of HORTON: Lessons Learned in a Decade of Scientific Software Development

M. Chan, T. Verstraelen, A. Tehrani, M. Richer, X. D. Yang, T. D. Kim, E. Vohringer-Martinez, F. Heidar-Zadeh, P. W. Ayers
Journal of Chemical Physics
Volume 160, Issue 16
2024
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Abstract 

HORTON is a free and open-source electronic-structure package written primarily in Python 3 with some underlying C++ components. While HORTON’s development has been mainly directed by the research interests of its leading contributing groups, it is designed to be easily modified, extended, and used by other developers of quantum chemistry methods or post-processing techniques. Most importantly, HORTON adheres to modern principles of software development, including modularity, readability, flexibility, comprehensive documentation, automatic testing, version control, and quality-assurance protocols. This article explains how the principles and structure of HORTON have evolved since we started developing it more than a decade ago. We review the features and functionality of the latest HORTON release (version 2.3) and discuss how HORTON is evolving to support electronic structure theory research for the next decade. Keywords: quantum chemistry software, computational chemistry, Hartree-Fock method, model hamiltonians, Density Functional Theory (DFT) methods, numerical integration grids, periodic boundary conditions, Gaussian integrals, atoms-inmolecules partitioning schemes, Hirshfeld partitioning, population analysis, electrostatic potential fitting, parsing and converting computational chemistry file formats, theoretical chemistry Python library

An information-theoretic approach to basis-set fitting of electron densities and other non-negative functions

A. Tehrani, J. S. M. Anderson, D. Chakraborty, J. I. Rodriguez-Hernandez, D. C. Thompson, T. Verstraelen, P. W. Ayers, F. Heidar-Zadeh
Journal of Computational Chemistry
2023
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Abstract 

The numerical ill-conditioning associated with approximating an electron density with a convex sum of Gaussian or Slater-type functions is overcome by using the (extended) Kullback–Leibler divergence to measure the deviation between the target and approximate density. The optimized densities are non-negative and normalized, and they are accurate enough to be used in applications related to molecular similarity, the topology of the electron density, and numerical molecular integration. This robust, efficient, and general approach can be used to fit any non-negative normalized functions (e.g., the kinetic energy density and molecular electron density) to a convex sum of non-negative basis functions. We present a fixed-point iteration method for optimizing the Kullback–Leibler divergence and compare it to conventional gradient-based optimization methods. These algorithms are released through the free and open-source BFit package, which also includes a L2-norm squared optimization routine applicable to any square-integrable scalar function.

Green Open Access

Constrained iterative Hirshfeld charges: A variational approach

L. Pujal, M. Van Zyl, E. Vohringer-Martinez, T. Verstraelen, P. Bultinck, P.W. Ayers, F. Heidar-Zadeh
Journal of Chemical Physics
Volume 156, Issue 19
2022
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Abstract 

We develop a variational procedure for the iterative Hirshfeld (HI) partitioning scheme. The main practical advantage of having a variational framework is that it provides a formal and straightforward approach for imposing constraints (e.g., fixed charges on certain atoms or molecular fragments) when computing HI atoms and their properties. Unlike many other variants of the Hirshfeld partitioning scheme, HI charges do not arise naturally from the information-theoretic framework, but only as a reverse-engineered construction of the objective function. However, the procedure we use is quite general and could be applied to other problems as well. We also prove that there is always at least one solution to the HI equations, but we could not prove that its self-consistent equations would always converge for any given initial pro-atom charges. Our numerical assessment of the constrained iterative Hirshfeld method shows that it satisfies many desirable traits of atoms in molecules and has the potential to surpass existing approaches for adding constraints when computing atomic properties.

Published under an exclusive license by AIP Publishing.

Fanpy: A Python Library for Prototyping Multideterminant Methods in Ab Initio Quantum Chemistry

T. D. Kim, M. Richer, G. Sánchez-Díaz, F. Heidar-Zadeh, T. Verstraelen, R.A. Miranda-Quintana, P.W. Ayers
Journal of Computational Chemistry
44, 5, 697-709
2022
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Abstract 

Fanpy is a free and open-source Python library for developing and testing multideterminant wavefunctions and related ab initio methods in electronic structure theory. The main use of Fanpy is to quickly prototype new methods by making it easier to transfer the mathematical conception of a new wavefunction ans¨atze to a working implementation. Fanpy uses the framework of our recently introduced Flexible Ansatz for N-electron Configuration Interaction (FANCI), where multideterminant wavefunctions are represented by their overlaps with Slater determinants of orthonormal spin-orbitals. In the simplest case, a new wavefunction ansatz can be implemented by simply writing a function for evaluating its overlap with an arbitrary Slater determinant. Fanpy is modular in both implementation and theory: the wavefunction model, the system’s Hamiltonian, and the choice of objective function are all independent modules. This modular structure makes it easy for users to mix and match different methods and for developers to quickly try new ideas. Fanpy is written purely in Python with standard dependencies, making it accessible for most operating systems; it adheres to principles of modern software development, including comprehensive documentation, extensive testing, and continuous integration and delivery protocols. This article is considered to be the official release notes for the Fanpy library.

IOData: A python library for reading, writing, and converting computational chemistry file formats and generating input files

T. Verstraelen, W. Adams, L. Pujal, A. Tehrani, B. D. Kelly, L. Macaya, F. Meng, M. Richer, R. Hernández-Esparza, X. D. Yang, M. Chan, T. D. Kim, M. Cools-Ceuppens, V. Chuiko, E. Vohringer-Martinez, P.W. Ayers, F. Heidar-Zadeh
Journal of Computational Chemistry
45, 6, 458--464
2021
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Abstract 

IOData is a free and open‐source Python library for parsing, storing, and converting various file formats commonly used by quantum chemistry, molecular dynamics, and plane‐wave density‐functional‐theory software programs. In addition, IOData supports a flexible framework for generating input files for various software packages. While designed and released for stand‐alone use, its original purpose was to facilitate the interoperability of various modules in the HORTON and ChemTools software packages with external (third‐party) molecular quantum chemistry and solid‐state density‐functional‐theory packages. IOData is designed to be easy to use, maintain, and extend; this is why we wrote IOData in Python and adopted many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. This article is the official release note of the IOData library.

Information-Theoretic Approaches to Atoms-in-Molecules: Hirshfeld Family of Partitioning Schemes

F. Heidar-Zadeh, P.W. Ayers, T. Verstraelen, I. Vinogradov, E. Vohringer-Martinez, P. Bultinck
Journal of Physical Chemistry A
112 (17) 4219-4245
2018
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Abstract 

Many population analysis methods are based on the precept that molecules should be built from fragments (typically atoms) that maximally resemble the isolated fragment. The resulting molecular building blocks are intuitive (because they maximally resemble well-understood systems) and transferable (because if two molecular fragments both resemble an isolated fragment, they necessarily resemble each other). Information theory is one way to measure the deviation between molecular fragments and their isolated counterparts, and it is a way that lends itself to interpretation. For example, one can analyze the relative importance of electron transfer and polarization of the fragments. We present key features, advantages, and disadvantages of the information-theoretic approach. We also codify existing information-theoretic partitioning methods in a way, that clarifies the enormous freedom one has within the information-theoretic ansatz.

The local response of global descriptors

F. Heidar-Zadeh, S. Fias, E. Vohringer-Martinez, T. Verstraelen, P.W. Ayers
Theoretical Chemistry Accounts
136 (1), 19
2017
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Abstract 

We consider the problem of defining an appropriate local descriptor corresponding to an arbitrary global descriptor. Although it does not seem easy to rigorously and uniquely define local analogues of derived global descriptors (e.g., the electrophilicity) or the fundamental global descriptors associated with the canonical ensemble (e.g., the hardness), the local response of these global descriptors can be defined unambiguously. We look at the local response of the global electrophilicity and compare it to the conventional, ad hoc, definition of the local electrophilicity. The local response of global nucleofugality and electrofugality is also discussed.

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