Special Issue "Design of Nanomaterials by Computer Simulation and Artificial Intelligence Approaches"
Deadline for manuscript submissions: 31 December 2022 | Viewed by 987
Interests: computational materials science; functional nanomaterials; materials informatics
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In the past century, the process of design and development of new materials underwent discovery, optimization, system design and manufacturing, which takes 10–20 years or more. Materials informatics, which is based on statistical algorithms, machine learning and artificial intelligence (AI) approaches, has become the fourth paradigm in materials design and development. It could accelerate the process and shorten the development cycle by 2–5 times.
We are pleased to invite you to contribute to this Special Issue on the design of nanomaterials by computer simulation and artificial intelligence approaches, which focuses on tackling the discovery, optimization and synthesis of nanomaterials with unique or improved properties compared to their bulk counterparts.
This Special Issue aims to provide a platform for the publication of research work related to the design and development of nano-sized (nanoparticles, nanowires, two-dimensional materials, thin films, nanocomposites, nanostructured materials) materials (superconductors, piezoelectric, thermoelectric and multiferroic materials, photovoltaic materials, catalysts, materials for electrochemical energy storage, advanced structural materials) by integrated computer simulation methods (ab initio simulation, molecular dynamics, Monte Carlo method, high-throughput simulation) or/and AI incorporating other methods such as high-throughput experiments.
Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Computer simulation on the complexity (in phase, chemical composition and thermodynamics) of surfaces, interfaces or grain boundaries of nanomaterials;
- Prediction on novel physical and chemical properties of nanomaterials by ab initio simulation.
- (Big) data-driven prediction of novel nanomaterials.
- High-throughput simulation studies on microstructure-property relationships in nanostructured materials.
- Studies on the physical and chemical properties of nanomaterials that use machine learning or deep learning methods.
We look forward to receiving your contributions.
Dr. Guang-Ping Zheng
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 2400 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.
- Ab initio simulation
- high-throughput simulation and algorithm
- phase-field simulation
- data-driven prediction on materials
- machine learning for inter-atomic potentials
- machine learning and deep learning methods
- nanostructured materials