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Computation
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Feature Papers in Computational Chemistry
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Feature Papers in Computational Chemistry

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Chemistry".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 2155

Special Issue Editors

Dr. Alexander S. Novikov

E-Mail Website
Guest Editor
Institute of Chemistry, Saint Petersburg State University, Universitetskii pr., 26, Petergof, 198504 St. Petersburg, Russia
Interests: quantum and computational chemistry; inorganic and coordination chemistry; organometallic chemistry; organic chemistry; catalysis; non-covalent interactions; machine learning and artificial intelligence in chemistry
Special Issues, Collections and Topics in MDPI journals
Dr. Felipe Fantuzzi

E-Mail Website
Guest Editor
Chemistry and Forensic Science, School of Natural Sciences, University of Kent, Canterbury CT2 7NH, UK
Interests: electronic structure; density functional theory; chemical bond theory; quantum chemistry; main group and organometallic chemistry; astrochemistry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This special issue on "Feature Papers in Computational Chemistry" aims to showcase cutting-edge research and advancements in the field of computational chemistry. We invite contributions that explore a diverse range of topics, including but not limited to quantum chemistry, molecular modeling, and the integration of artificial intelligence within computational frameworks.

The issue will highlight innovative methodologies and applications in areas such as the electronic structure of molecules and materials, catalysis, photochemistry, chemical dynamics, and computational drug design. Serving as a platform for leading researchers in computational chemistry, this collection aims to foster collaboration and dialogue, encouraging the exchange of ideas and enhancing the visibility and impact of their work.

We encourage submissions that not only present novel research but also provide comprehensive reviews that synthesize current trends and future directions in computational chemistry. This initiative seeks to establish novel connections between theoretical concepts and practical applications, ultimately contributing to the advancement of the discipline.

Dr. Alexander S. Novikov
Dr. Felipe Fantuzzi
Guest Editors

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. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). 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

  • computational chemistry
  • quantum chemistry
  • DFT
  • QTAIM
  • ab initio
  • machine learning
  • big data
  • artificial intelligence
  • modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

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27 pages, 872 KiB  
Article
Effect of Monomer Mixture Composition on TiCl4-Al(i-C4H9)3 Catalytic System Activity in Butadiene–Isoprene Copolymerization: A Theoretical Study
by Konstantin A. Tereshchenko, Rustem T. Ismagilov, Nikolai V. Ulitin, Yana L. Lyulinskaya and Alexander S. Novikov
Computation 2025, 13(8), 184; https://doi.org/10.3390/computation13080184 (registering DOI) - 1 Aug 2025
Abstract
Divinylisoprene rubber, a copolymer of butadiene and isoprene, is used as raw material for rubber technical products, combining isoprene rubber’s elasticity and butadiene rubber’s wear resistance. These properties depend quantitatively on the copolymer composition, which depends on the kinetics of its synthesis. This [...] Read more.
Divinylisoprene rubber, a copolymer of butadiene and isoprene, is used as raw material for rubber technical products, combining isoprene rubber’s elasticity and butadiene rubber’s wear resistance. These properties depend quantitatively on the copolymer composition, which depends on the kinetics of its synthesis. This work aims to theoretically describe how the monomer mixture composition in the butadiene–isoprene copolymerization affects the activity of the TiCl4–Al(i-C4H9)3 catalytic system (expressed by active sites concentration) via kinetic modeling. This enables development of a reliable kinetic model for divinylisoprene rubber synthesis, predicting reaction rate, molecular weight, and composition, applicable to reactor design and process intensification. Active sites concentrations were calculated from experimental copolymerization rates and known chain propagation constants for various monomer compositions. Kinetic equations for active sites formation were based on mass-action law and Langmuir monomolecular adsorption theory. An analytical equation relating active sites concentration to monomer composition was derived, analyzed, and optimized with experimental data. The results show that monomer composition’s influence on active sites concentration is well described by a two-step kinetic model (physical adsorption followed by Ti–C bond formation), accounting for competitive adsorption: isoprene adsorbs more readily, while butadiene forms more stable active sites. Full article
(This article belongs to the Special Issue Feature Papers in Computational Chemistry)
17 pages, 2016 KiB  
Article
DFT-Guided Next-Generation Na-Ion Batteries Powered by Halogen-Tuned C12 Nanorings
by Riaz Muhammad, Anam Gulzar, Naveen Kosar and Tariq Mahmood
Computation 2025, 13(8), 180; https://doi.org/10.3390/computation13080180 (registering DOI) - 1 Aug 2025
Abstract
Recent research on the design and synthesis of new and upgraded materials for secondary batteries is growing to fulfill future energy demands around the globe. Herein, by using DFT calculations, the thermodynamic and electrochemical properties of Na/Na+@C12 complexes and then [...] Read more.
Recent research on the design and synthesis of new and upgraded materials for secondary batteries is growing to fulfill future energy demands around the globe. Herein, by using DFT calculations, the thermodynamic and electrochemical properties of Na/Na+@C12 complexes and then halogens (X− = Br−, Cl−, and F−) as counter anions are studied for the enhancement of Na-ion battery cell voltage and overall performance. Isolated C12 nanorings showed a lower cell voltage (−1.32 V), which was significantly increased after adsorption with halide anions as counter anions. Adsorption of halides increased the Gibbs free energy, which in turn resulted in higher cell voltage. Cell voltage increased with the increasing electronegativity of the halide anion. The Gibbs free energy of Br−@C12 was −52.36 kcal·mol−1, corresponding to a desirable cell voltage of 2.27 V, making it suitable for use as an anode in sodium-ion batteries. The estimated cell voltage of these considered complexes ensures the effective use of these complexes in sodium-ion secondary batteries. Full article
(This article belongs to the Special Issue Feature Papers in Computational Chemistry)
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23 pages, 4276 KiB  
Article
First-Principles Insights into Mo and Chalcogen Dopant Positions in Anatase, TiO2
by W. A. Chapa Pamodani Wanniarachchi, Ponniah Vajeeston, Talal Rahman and Dhayalan Velauthapillai
Computation 2025, 13(7), 170; https://doi.org/10.3390/computation13070170 - 14 Jul 2025
Viewed by 232
Abstract
This study employs density functional theory (DFT) to investigate the electronic and optical properties of molybdenum (Mo) and chalcogen (S, Se, Te) co-doped anatase TiO2. Two co-doping configurations were examined: Model 1, where the dopants are adjacent, and Model 2, where [...] Read more.
This study employs density functional theory (DFT) to investigate the electronic and optical properties of molybdenum (Mo) and chalcogen (S, Se, Te) co-doped anatase TiO2. Two co-doping configurations were examined: Model 1, where the dopants are adjacent, and Model 2, where the dopants are farther apart. The incorporation of Mo into anatase TiO2 resulted in a significant bandgap reduction, lowering it from 3.22 eV (pure TiO2) to range of 2.52–0.68 eV, depending on the specific doping model. The introduction of Mo-4d states below the conduction band led to a shift in the Fermi level from the top of the valence band to the bottom of the conduction band, confirming the n-type doping characteristics of Mo in TiO2. Chalcogen doping introduced isolated electronic states from Te-5p, S-3p, and Se-4p located above the valence band maximum, further reducing the bandgap. Among the examined configurations, Mo–S co-doping in Model 1 exhibited most optimal structural stability structure with the fewer impurity states, enhancing photocatalytic efficiency by reducing charge recombination. With the exception of Mo–Te co-doping, all co-doped systems demonstrated strong oxidation power under visible light, making Mo-S and Mo-Se co-doped TiO2 promising candidates for oxidation-driven photocatalysis. However, their limited reduction ability suggests they may be less suitable for water-splitting applications. The study also revealed that dopant positioning significantly influences charge transfer and optoelectronic properties. Model 1 favored localized electron density and weaker magnetization, while Model 2 exhibited delocalized charge density and stronger magnetization. These findings underscore the critical role of dopant arrangement in optimizing TiO2-based photocatalysts for solar energy applications. Full article
(This article belongs to the Special Issue Feature Papers in Computational Chemistry)
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14 pages, 2188 KiB  
Article
CrystalShift: A Versatile Command-Line Tool for Crystallographic Structural Data Analysis, Modification, and Format Conversion Prior to Solid-State DFT Calculations of Organic Crystals
by Ilona A. Isupova and Denis A. Rychkov
Computation 2025, 13(6), 138; https://doi.org/10.3390/computation13060138 - 4 Jun 2025
Viewed by 1119
Abstract
CrystalShift is an open-source computational tool tailored for the analysis, transformation, and conversion of crystallographic data, with a particular emphasis on organic crystal structures. It offers a comprehensive suite of features valuable for the computational study of solids: format conversion, crystallographic basis transformation, [...] Read more.
CrystalShift is an open-source computational tool tailored for the analysis, transformation, and conversion of crystallographic data, with a particular emphasis on organic crystal structures. It offers a comprehensive suite of features valuable for the computational study of solids: format conversion, crystallographic basis transformation, atomic coordinate editing, and molecular layer analysis. These options are especially valuable for studying the mechanical properties of molecular crystals with potential applications in organic materials science. Written in the C programming language, CrystalShift offers computational efficiency and compatibility with widely used crystallographic formats such as CIF, POSCAR, and XYZ. It provides a command-line interface, enabling seamless integration into research workflows while addressing specific challenges in crystallography, such as handling non-standard file formats and robust error correction. CrystalShift may be applied for both in-depth study of particular crystal structure origins and the high-throughput conversion of crystallographic datasets prior to DFT calculations with periodic boundary conditions using VASP code. Full article
(This article belongs to the Special Issue Feature Papers in Computational Chemistry)
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Review

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23 pages, 309 KiB  
Review
Mathematical Optimization in Machine Learning for Computational Chemistry
by Ana Zekić
Computation 2025, 13(7), 169; https://doi.org/10.3390/computation13070169 - 11 Jul 2025
Viewed by 418
Abstract
Machine learning (ML) is transforming computational chemistry by accelerating molecular simulations, property prediction, and inverse design. Central to this transformation is mathematical optimization, which underpins nearly every stage of model development, from training neural networks and tuning hyperparameters to navigating chemical space for [...] Read more.
Machine learning (ML) is transforming computational chemistry by accelerating molecular simulations, property prediction, and inverse design. Central to this transformation is mathematical optimization, which underpins nearly every stage of model development, from training neural networks and tuning hyperparameters to navigating chemical space for molecular discovery. This review presents a structured overview of optimization techniques used in ML for computational chemistry, including gradient-based methods (e.g., SGD and Adam), probabilistic approaches (e.g., Monte Carlo sampling and Bayesian optimization), and spectral methods. We classify optimization targets into model parameter optimization, hyperparameter selection, and molecular optimization and analyze their application across supervised, unsupervised, and reinforcement learning frameworks. Additionally, we examine key challenges such as data scarcity, limited generalization, and computational cost, outlining how mathematical strategies like active learning, meta-learning, and hybrid physics-informed models can address these issues. By bridging optimization methodology with domain-specific challenges, this review highlights how tailored optimization strategies enhance the accuracy, efficiency, and scalability of ML models in computational chemistry. Full article
(This article belongs to the Special Issue Feature Papers in Computational Chemistry)
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