molecules-logo

Journal Browser

Journal Browser

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 July 2024 | Viewed by 2029

Special Issue Editors

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

E-Mail Website
Guest Editor
Key Lab of Colloid and Interface Chemistry, 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

Related Special Issue

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

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
Viewed by 489
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)
Show Figures

Graphical abstract

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 604
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)
Show Figures

Figure 1

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 726
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)
Show Figures

Figure 1

Back to TopTop