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Advances in Molecular Modeling in Chemistry, 2nd Edition
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Advances in Molecular Modeling in Chemistry, 2nd Edition

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 4164

Special Issue Editors

Dr. Heng Zhang

E-Mail Website
Guest Editor
School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
Interests: physical chemistry of surfactant; computer simulation about surface science; molecular simulation on self-assemble system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shiling Yuan

E-Mail Website
Guest Editor
School of Chemistry and Chemical Engineering, Shandong University, Jinan 250100, China
Interests: molecular simulation on polymer; surfactant; self-assembly
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Molecular modeling plays a crucial role in chemistry investigations. With the development of computing powers, large-scale simulations can be achieved. Molecular modeling has been applied successfully in many areas of chemistry, for example, the behavior of liquid solutions, proteins, DNA, polysaccharides, lipid membranes, crystals, amorphous solids, or any combination of them, the process of adsorption or desorption at interfaces, protein folding, self-assembly, etc.

Aside from the widespread application of molecular modeling, the techniques of simulation have also developed rapidly. Many simulation techniques emerged, including ab initio molecular dynamics, polarizable force field, reactive molecular dynamics, machine learning accelerated simulation, metadynamics, etc.

This Special Issue invites original papers and reviews reporting molecular simulation studies including quantum chemistry calculation, molecular dynamic simulation, Monte Carlo simulation, combined experimental and simulation studies, etc. This issue also welcomes focused review articles that examine the state of the art, identify emerging trends, and suggest future directions for the application of molecular modeling methods.

Dr. Heng Zhang
Prof. Dr. Shiling Yuan
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. Molecules is an international peer-reviewed open access semimonthly 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 2700 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

  • molecular modeling
  • applications
  • quantum chemistry calculation
  • molecular dynamic simulation
  • Monte Carlo simulation
  • combined experimental and simulation studies

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Related Special Issue

Published Papers (4 papers)

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Research

12 pages, 5042 KiB  
Article
Experiment and Molecular Dynamic Simulation on Interactions between 3,4-Bis(3-nitrofurazan-4-yl) Furoxan (DNTF) and Some Low-Melting-Point Explosives
by Junming Yuan, Runsheng Huang, Jinying Wang, Xiwei Xing, Jing Wang, Tao Han, Qi Yang and Jia Yang
Molecules 2024, 29(16), 3757; https://doi.org/10.3390/molecules29163757 - 8 Aug 2024
Viewed by 578
Abstract
3,4-bis(3-nitrofurazan-4-yl) furoxan (DNTF) is an explosive with excellent performance, and the use of DNTF as a high-energy component is of great significance for improving the comprehensive performance of weapons. To explore the effect of DNTF on low-melting-point molten carrier explosives, the compatibility between [...] Read more.
3,4-bis(3-nitrofurazan-4-yl) furoxan (DNTF) is an explosive with excellent performance, and the use of DNTF as a high-energy component is of great significance for improving the comprehensive performance of weapons. To explore the effect of DNTF on low-melting-point molten carrier explosives, the compatibility between DNTF and other low-melting-point explosives was analyzed by differential scanning calorimetry, and mechanical sensitivity was tested. The compatibility and cohesive energy density between DNTF and other low-melting-point explosives were calculated by Materials Studio. The results showed that DNTF has good compatibility with most low-melting-point explosives, and the peak temperature change of the mixed system formed by melt-casting is not obvious. Among them, DNTF has the best compatibility with MTNP, TNT, and DNAN; moderate compatibility with DFTNAN and DNP; and the worst compatibility with DNMT. The sensitivity test results indicate that the combination of DNTF and TNT has the most significant reduction in mechanical sensitivity. DFTNAN and MTNP have better stability than DNTF and can generate strong interaction forces with DNTF. Other low-melting-point explosives mixed with DNTF have lower intermolecular forces than DNTF. The DNTF/MTNP system requires the most energy to phase change when heated compared to other mixed systems and is the least sensitive to heat. The DNTF/DNMT system has the lowest cohesive energy density and is the most sensitive to heat. Full article
(This article belongs to the Special Issue Advances in Molecular Modeling in Chemistry, 2nd Edition)
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16 pages, 6982 KiB  
Article
Microscopic Understanding of Interfacial Performance and Antifoaming Mechanism of REP Type Block Polyether Nonionic Surfactants
by Yifei Zhao, Chunlong Xue, Deluo Ji, Weiqian Gong, Yue Liu and Ying Li
Molecules 2024, 29(8), 1816; https://doi.org/10.3390/molecules29081816 - 17 Apr 2024
Cited by 1 | Viewed by 1026
Abstract
In many practical applications involving surfactants, achieving defoaming without affecting interfacial activity is a challenge. In this study, the antifoaming performance of REP-type block polymer nonionic surfactant C12EOmPOn was determined, and molecular dynamics simulation method was employed to investigate the molecular behaviors of [...] Read more.
In many practical applications involving surfactants, achieving defoaming without affecting interfacial activity is a challenge. In this study, the antifoaming performance of REP-type block polymer nonionic surfactant C12EOmPOn was determined, and molecular dynamics simulation method was employed to investigate the molecular behaviors of surfactants at a gas/water interface, the detailed arrangement information of the different structural segments of the surfactant molecules and the inter-/intra-interactions between all the structural motifs in the interfacial layer were analyzed systematically, by which the antifoaming mechanisms of the surfactants were revealed. The results show that the EO and PO groups of REP-type polyether molecules are located in the aqueous phase near the interface, and the hydrophobic tails distribute separately, lying almost flat on the gas/water interface. The interaction between the same groups of EOs and POs is significantly stronger than with water. REP block polyethers with high polymerization degrees of EO and PO are more inclined to overlap into dense layers, resulting in the formation of aggregates resembling “oil lenses” spreading on the gas/water interface, which exerts a stronger antifoaming effect. This study provides a smart approach to obtaining efficient antifoaming performance at room temperature without adding other antifoam ingredients. Full article
(This article belongs to the Special Issue Advances in Molecular Modeling in Chemistry, 2nd Edition)
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16 pages, 13158 KiB  
Article
Atomistic Insights into the Influence of High Concentration H2O2/H2O on Al Nanoparticles Combustion: ReaxFF Molecules Dynamics Simulation
by Xindong Yu, Pengtu Zhang, Heng Zhang and Shiling Yuan
Molecules 2024, 29(7), 1567; https://doi.org/10.3390/molecules29071567 - 31 Mar 2024
Viewed by 889
Abstract
The combination of Al nanoparticles (ANPs) as fuel and H2O2 as oxidizer is a potential green space propellant. In this research, reactive force field molecular dynamics (ReaxFF-MD) simulations were used to study the influence of water addition on the combustion [...] Read more.
The combination of Al nanoparticles (ANPs) as fuel and H2O2 as oxidizer is a potential green space propellant. In this research, reactive force field molecular dynamics (ReaxFF-MD) simulations were used to study the influence of water addition on the combustion of Al/H2O2. The MD results showed that as the percentage of H2O increased from 0 to 30%, the number of Al-O bonds on the ANPs decreased, the number of Al-H bonds increased, and the adiabatic flame temperature of the system decreased from 4612 K to 4380 K. Since the Al-O bond is more stable, as the simulation proceeds, the number of Al-O bonds will be significantly higher than that of Al-H and Al-OH bonds, and the Al oxides (Al[O]x) will be transformed from low to high coordination. Subsequently, the combustion mechanism of the Al/H2O2/H2O system was elaborated from an atomic perspective. Both H2O2 and H2O were adsorbed and chemically activated on the surface of ANPs, resulting in molecular decomposition into free radicals, which were then captured by ANPs. H2 molecules could be released from the ANPs, while O2 could not be released through this pathway. Finally, it was found that the coverage of the oxide layer reduced the rate of H2O2 consumption and H2 production significantly, simultaneously preventing the deformation of the Al clusters’ morphology. Full article
(This article belongs to the Special Issue Advances in Molecular Modeling in Chemistry, 2nd Edition)
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21 pages, 7144 KiB  
Article
Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery
by Chun Zhang, Liangxu Xie, Xiaohua Lu, Rongzhi Mao, Lei Xu and Xiaojun Xu
Molecules 2024, 29(7), 1499; https://doi.org/10.3390/molecules29071499 - 27 Mar 2024
Viewed by 1253
Abstract
Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role [...] Read more.
Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes. Full article
(This article belongs to the Special Issue Advances in Molecular Modeling in Chemistry, 2nd Edition)
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