Molecular Dynamics Simulations and Modelling of Gels
A special issue of Gels (ISSN 2310-2861). This special issue belongs to the section "Gel Analysis and Characterization".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 146
Special Issue Editors
Interests: polymer physics; molecular dynamics simulation; polymer networks/gels; machine learning in polymer
Interests: polymer physics; polymer reactions; polymer composite; graphene; computation; molecular simulation; 2D material; soft material
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue on “Molecular Dynamics Simulations and Modelling of Gels” aims to represent recent, cutting-edge research that combines computational simulations, data-driven theoretical developments, and experimental support to deepen our understanding of gels. The complex and rich structures of gels result in unique properties, making them suitable for various applications, such as biomedicine, smart materials, and environmental control. The functionality of these use-inspired applications highlights the importance of a detailed understanding of gel structure to advance the design of gel materials.
As a powerful tool, molecular dynamics (MD) simulation provides detailed insights into the atomic and molecular behavior of gels. MD simulations can reveal the properties determined by the gel's internal structures by simulating gel behaviors at the atomic or molecular level. This approach is essential for guiding the design of new gel materials with expected properties for specific applications.
In recent years, integrating machine learning (ML) and artificial intelligence (AI) with molecular dynamics has expanded the potential for research. These advanced techniques can significantly enhance the predictive power and efficiency of simulations. ML algorithms can analyze large amounts of simulation data to identify patterns and predict material properties, while AI can optimize simulation parameters and improve the accuracy of theoretical models.
This Special Issue invites contributions exploring MD simulation and modeling for decoding structure–property relationships. It also particularly encourages studies incorporating ML and AI techniques, offering innovative approaches to simulation and modeling. Experimental studies that support and validate computational findings are also highly welcome.
Dr. Yuan Tian
Dr. Zilu Wang
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. Gels 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 2100 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 dynamics (MD) simulation
- gel modeling
- gel material design
- gel structure–property relationships
- machine learning (ML)/artificial intelligence (AI) for gels
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