Special Issue "Deep Learning for Nanomaterials"
A special issue of Nanomaterials (ISSN 2079-4991).
Deadline for manuscript submissions: closed (30 April 2020).
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.
Prof. Dr. Steve Bull
Prof. Dr. John Fitzgerald
Manuscript Submission Information
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