Advances in Computational Materials Science on Functional Interfaces and Surfaces, Volume II
A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Theory and Simulation of Nanostructures".
Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1416
Special Issue Editor
Interests: battery/super capacitor/fuel-cell; CCS (carbon dioxide capture & sequestration); super absorbent polymer; hybrid interface design for composites
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Based on the successful Special Issue “Advances in Computational Materials Science on Functional Interfaces and Surfaces”, we have set up a second volume.
In the future, research on high-value-added products with new functions, using materials created by the fusion of science and technology, will be widely and actively conducted. Related industrial fields are expected to be reorganized in accordance with the appearance of new industries. Although nanomaterials have been researched and their activated surfaces show more unique attributes than existing materials, there are constraints to applying them to the industry due to the difficulty in materializing other complex functions with them and their small volumes. The hybridization of heterogeneous materials or heterogeneous scales is a new materials technology that has been assessed as a new technology with which to create various functional materials. Computational materials science enables a functional interface and surface in order to design, invent, and forecast nanomaterials’ properties by using computer simulation techniques such as density functional theory (DFT), molecular dynamics (MD), the Monte Carlo (MC) method, finite element methods (FEMs), and machine learning (ML) approaches. All topics potentially falling into the category of computational materials science will be considered, including inorganic materials (metals, ceramics, composites, semiconductors, nanostructures, 2D materials, metamaterials, etc.), organic materials (polymers, liquid crystals, surfactants, emulsions, etc.), and hybrid materials of inorganic as well as organic components. Original research articles, in the form of full papers or communications, and reviews are both welcome.
Prof. Dr. Seung Geol Lee
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. Nanomaterials 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 2900 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
- functional materials
- functional interfaces and surfaces
- computational materials science
- first principle calculations
- density functional theory (DFT)
- molecular dynamics (MD)
- Monte Carlo (MC) method
- mesoscale simulations
- finite element methods (FEM)
- multiscale simulations
- machine learning (ML)
