Special Issue "Deep Learning for Nanomaterials"

A special issue of Nanomaterials (ISSN 2079-4991).

Deadline for manuscript submissions: closed (30 April 2020).

Special Issue Editors

Dr. Florian Heberle
E-Mail Website
Guest Editor
Senior Academic Councillor, Center of Energy Technology, University of Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany
Prof. Dr. Steve Bull
E-Mail Website
Guest Editor
Cookson Group Chair of Eng Materials, School of Engineering, Bedson Building, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom
Prof. Dr. John Fitzgerald
E-Mail Website
Guest Editor
Head of the School of Computing, School of Computing, Newcastle University, Newcastle upon TyneNE1 7RU, United Kingdom

Special Issue Information

Dear Colleagues,

Nanomaterials are composed of unbound particles in an aggregate state with one or more external dimensions. They are developed to exhibit novel characteristics such as improved toughness, increased permeability, etc. when compared to their counterparts. They are manufactured and used at an insignificant scale. Applications of nanomaterials are widespread in various industries, especially automotive, construction, medicine, etc. To understand the properties of nanomaterials for every field, we require innovative learning techniques.

Deep learning is a learning technique that teaches computers to do what comes naturally to humans. It structures the algorithm in layers to build an artificial neural network (ANN) that can learn and make an intelligent decision on its own. Here, a computer is modeled to learn and perform classification tasks directly from text, images or sounds, and the models are trained to achieve accuracy and performance at higher levels. Recent advances in deep learning have outperformed humans in tasks similar to classifying objects in images. Models are trained by a large set of labeled data and neural network architectures that learn features directly from data with no manual feature extraction. Deep learning for nanomaterials combines artificial neural networks (ANN) with principle component analysis (PCA), which simplifies the input data to a neural network, de-correlates the data, and reduces the number of independent variables. Similar to the way the brain, which solves problems using many interconnected neurons, inspired ANN, the structural properties of nanomaterials are processed by ANN with suitable algorithms to determine the morphology by quantifying the structural properties. 

Since nanomaterials have the potential to change the future of product manufacturing, with intelligent learning methodologies implemented along with natural or engineered nanomaterials, the growth will be exponential. Deep learning enhances the implementation performances for any type of application. Advancements in nanomaterials and nanotechnology in recent years have created massive interest among research personnel. This Special Issue on Deep Learning for Nanomaterials invites new experimental learning and application-based concepts that are best suited and applied to the nonmaterial-based environment.

Topics of interest include but are not restricted to:

  • Development trends of nanomaterials concerning the future of the healthcare sector;
  • Computational design of nanomaterials with deep learning neural networks;
  • An insight into efficient learning for nanoparticles;
  • Representing materials and molecular data for deep learning;
  • Design and assessment of nanomaterials with high accuracy;
  • Problems and perspectives in structural properties of nanomaterials;
  • Design and discovery of nanomaterials through deep learning concepts;
  • Efficient large-scale deep learning algorithms for nanomaterials to achieve accuracy and performance level;
  • In-depth analysis of the classification of nanomaterials;
  • Need for advanced learning between engineered nanomaterial and environmental media.
Dr. Florian Heberle
Prof. Dr. Steve Bull
Prof. Dr. John Fitzgerald
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 papers will be 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 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 2200 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.

Published Papers

There is no accepted submissions to this special issue at this moment.
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