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Advances in Density Functional Theory and Related Methods for Molecular Computational Chemistry

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 4454

Special Issue Editor

Dr. Paolo Umari

E-Mail Website
Guest Editor
Department of Physics and Astronomy, University of Padova, Via F. Marzolo 8, 35131 Padua, Italy
Interests: density functional theory; photoelectric material; computational spectroscopy; chemical physics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For more than four decades, density functional theory (DFT) has been the most popular method for electronic structure studies. A large variety of molecular properties can now be evaluated from first principles. This together with the progress in high-performance computing now permits the in silico design of new compounds with tailored properties. These successes are the outcome of continuous research on DFT methods. This research aims to achieve several goals: the improvement of accuracy, for example, by introducing novel and more complex exchange and correlation functionals, the enlargement of the scope of calculations, for example, by devising schemes for simulating novel spectroscopies or tackling complex reaction mechanisms, the introduction of new algorithms for speeding-up calculations or improving computational scaling, and the automatization of DFT workflows for producing large databases to be used for the discovery of materials. DFT is also used as a starting point for more complex methods tackling excitation or correlation phenomena, for example, the GW-BSE approach and the quantum Monte Carlo methods. These methods are also the subject of intensive investigations.

The following Special Issue aims to provide an account of all of these efforts and will focus on computational chemistry and density functional theory in materials chemistry at the molecular scale, which involves predicting or evaluating the properties of a large variety of molecules and obtaining their equilibrium structure. We encourage contributions in the broad field of methodological and computational developments in DFT and its related methods and we also look forward to contributions focused on the application of advanced DFT methods in molecular systems. It should be noted that pure computational or pure model studies will not be suitable for this Special Issue.

Dr. Paolo Umari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • density functional theory
  • quantum chemistry
  • algorithms
  • quantum computing
  • high-performance computing
  • machine learning
  • many-body perturbations theory
  • quantum Monte Carlo

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Published Papers (3 papers)

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19 pages, 861 KiB  
Article
Prediction of 57Fe Mössbauer Nuclear Quadrupole Splittings with Hybrid and Double-Hybrid Density Functionals
by Yihao Zhang, Haonan Tang and Wenli Zou
Int. J. Mol. Sci. 2025, 26(6), 2821; https://doi.org/10.3390/ijms26062821 - 20 Mar 2025
Viewed by 368
Abstract
As a crucial parameter in Mössbauer spectroscopy, nuclear quadrupole splitting (NQS) exhibits a strong dependence on quantum chemistry methods, which makes accurate theoretical predictions challenging. Meanwhile, the continuous emergence of new density functionals presents opportunities to advance current NQS research. In this study, [...] Read more.
As a crucial parameter in Mössbauer spectroscopy, nuclear quadrupole splitting (NQS) exhibits a strong dependence on quantum chemistry methods, which makes accurate theoretical predictions challenging. Meanwhile, the continuous emergence of new density functionals presents opportunities to advance current NQS research. In this study, we evaluate the performance of eleven hybrid density functionals and twelve double-hybrid density functionals, selected from widely used functionals and newly developed functionals, in predicting the NQS values of the 57Fe nuclide for 32 iron-containing molecules within about 70 atoms. The calculations have incorporated scalar relativistic effects using the exact two-component (X2C) Hamiltonian. In general, the double-hybrid functional PBE-0DH demonstrates superior performance compared to the experimental values, achieving a mean absolute error (MAE) of 0.20 mm/s. Meanwhile, rSCAN38 is the best hybrid functional for our database with an MAE = 0.25 mm/s, and it offers a significant advantage in computational efficiency over PBE-0DH. The +/− sign of NQS has also been considered in our error statistics when it has a clear physical meaning; if neglected, the errors of many functionals decrease, but PBE-0DH and rSCAN38 remain unaffected. Notably, when calculating ferrocene [Fe(C5H5)2], which involves strong static correlations, all hybrid functionals that incorporate more than 10% exact exchange fail, while several double-hybrid functionals continue to deliver reliable results. In addition, we encountered two particularly challenging species characterized by strong static correlations: [Fe(H2O)5NO]2+ and FeO2−-porphyrin. Unfortunately, none of the density functionals tested in our study yielded satisfactory results for the two cases since the density functional theory (DFT) is a single-determinant approach, and it is imperative to explore large-scale multi-configurational methods for these species. This research offers valuable guidance for selecting density functionals in Mössbauer NQS calculations and serves as a reference point for the future development of new density functionals. Full article
(This article belongs to the Special Issue Advances in Density Functional Theory and Related Methods for Molecular Computational Chemistry)
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29 pages, 5608 KiB  
Article
Quantum Embedding of Non-Local Quantum Many-Body Interactions in an Prototypal Anti-Tumor Vaccine Metalloprotein on Near-Term Quantum Computing Hardware
by Elena Chachkarova, Terence Tse, Yordan Yordanov, Yao Wei and Cedric Weber
Int. J. Mol. Sci. 2025, 26(4), 1550; https://doi.org/10.3390/ijms26041550 - 12 Feb 2025
Viewed by 878
Abstract
The world obeys quantum physics and quantum computing presents an alternative way to map physical problems to systems that follow the same laws. Such computation fundamentally constitutes a better way to understand the most challenging quantum problems. One such problem is the accurate [...] Read more.
The world obeys quantum physics and quantum computing presents an alternative way to map physical problems to systems that follow the same laws. Such computation fundamentally constitutes a better way to understand the most challenging quantum problems. One such problem is the accurate simulation of highly correlated quantum systems. Still, modern-day quantum hardware has limitations and only allows for the modeling of simple systems. Here, we present for the first time a quantum computer model simulation of a complex hemocyanin molecule, which is an important respiratory protein involved in various physiological processes and is also used as a key component in therapeutic vaccines for cancer. To characterize the mechanism by which hemocyanin transports oxygen, variational quantum eigensolver (VQE) and quantum embedding methods are used in the context of dynamic mean field theory to solve the Anderson impurity model (AIM). Finally, it is concluded that the magnetic structure of hemocyanin is largely influenced by the many-body correction and that the computational effort for solving correlated electron systems could be substantially reduced with the introduction of quantum computing algorithms. We encourage the use of the Hamiltonian systems presented in this paper as a benchmark for testing quantum computing algorithms’ efficiency for chemistry applications. Full article
(This article belongs to the Special Issue Advances in Density Functional Theory and Related Methods for Molecular Computational Chemistry)
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21 pages, 5890 KiB  
Article
Molecular Modeling of Vasodilatory Activity: Unveiling Novel Candidates Through Density Functional Theory, QSAR, and Molecular Dynamics
by Anthony Bernal, Edgar A. Márquez, Máryury Flores-Sumoza, Sebastián A. Cuesta, José Ramón Mora, José L. Paz, Adel Mendoza-Mendoza, Juan Rodríguez-Macías, Franklin Salazar, Daniel Insuasty, Yovani Marrero-Ponce, Guillermin Agüero-Chapin, Virginia Flores-Morales and Domingo César Carrascal-Hernández
Int. J. Mol. Sci. 2024, 25(23), 12649; https://doi.org/10.3390/ijms252312649 - 25 Nov 2024
Viewed by 1230
Abstract
Cardiovascular diseases (CVD) pose a significant global health challenge, requiring innovative therapeutic strategies. Vasodilators, which are central to vasodilation and blood pressure reduction, play a crucial role in cardiovascular treatment. This study integrates quantitative structure– (QSAR) modeling and molecular dynamics (MD) simulations to [...] Read more.
Cardiovascular diseases (CVD) pose a significant global health challenge, requiring innovative therapeutic strategies. Vasodilators, which are central to vasodilation and blood pressure reduction, play a crucial role in cardiovascular treatment. This study integrates quantitative structure– (QSAR) modeling and molecular dynamics (MD) simulations to predict the biological activity and interactions of vasodilatory compounds with the aim to repurpose drugs already known and estimateing their potential use as vasodilators. By exploring molecular descriptors, such as electronegativity, softness, and highest occupied molecular orbital (HOMO) energy, this study identifies key structural features influencing vasodilatory effects, as it seems molecules with the same mechanism of actions present similar frontier orbitals pattern. The QSAR model was built using fifty-four Food Drugs Administration-approved (FDA-approved) compounds used in cardiovascular treatment and their activities in rat thoracic aortic rings; several molecular descriptors, such as electronic, thermodynamics, and topographic were used. The best QSAR model was validated through robust training and test dataset split, demonstrating high predictive accuracy in drug design. The validated model was applied on the FDA dataset and molecules in the application domain with high predicted activity were retrieved and filtered. Thirty molecules with the best-predicted pKI50 were further analyzed employing molecular orbital frontiers and classified as angiotensin-I or β1-adrenergic inhibitors; then, the best scoring values obtained from molecular docking were used to perform a molecular dynamics simulation, providing insight into the dynamic interactions between vasodilatory compounds and their targets, elucidating the strength and stability of these interactions over time. According to the binding energies results, this study identifies novel vasodilatory candidates where Dasabuvir and Sertindole seem to have potent and selective activity, offering promising avenues for the development of next-generation cardiovascular therapies. Finally, this research bridges computational modelling with experimental validation, providing valuable insight for the design of optimized vasodilatory agents to address critical unmet needs in cardiovascular medicine. Full article
(This article belongs to the Special Issue Advances in Density Functional Theory and Related Methods for Molecular Computational Chemistry)
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