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Artificial Intelligence (AI) in Nanoscience, Engineering and Biomedical Research
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Artificial Intelligence (AI) in Nanoscience, Engineering and Biomedical Research

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 6942

Special Issue Editor

Prof. Dr. Mehdi Keshavarz-hedayati

E-Mail Website
Guest Editor
Department of Engineering, Durham University, Durham DH13EL, UK
Interests: metamaterials; AI; photonics

Special Issue Information

Dear Colleagues,

The use of artificial intelligence is now a core part of almost every industry, from materials discovery to biomedical health.

The AI-driven materials discovery process involves predicting the best material for a particular application using machine learning algorithms. New materials that are cheaper, lighter, and stronger than those we currently use can be found this way. Using AI in biomedical health engineering for diagnosis and prognosis is another emerging use of AI.

Optimizing performance, design, or analysis is a complex, multidimensional problem that is challenging to resolve. This problem has been solved by Artificial Intelligence algorithms. These algorithms are able to find the optimal solution without user input, thus minimizing human error and saving research and design time.

As a result of the introduction of AI, the number of data-driven innovations in the medical field has exploded. It has allowed researchers to explore and develop solutions to problems they would have never been able to tackle before. In addition to modeling data, AI is used for generating hypotheses and performing predictive analytics. The latter shows great potential for next-generation materials and biomedical diagnosis and prognosis.

The aim of this Special Issue is to collect recent and cutting-edge developments in AI based engineering. Papers providing original results in AI driven design, fabrication and analysis of materials and AI in biomedical research, or closely related topics, are welcome.

Prof. Dr. Mehdi Keshavarz-hedayati
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. Applied Sciences 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.

Keywords

  • materials design using artificial intelligence
  • design of photonics materials using artificial intelligence
  • biomedical and engineering applications of artificial intelligence
  • inverse design of metamaterials
  • nanophotonics and optics
  • optimizing materials processing with artificial intelligence and machine learning

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Published Papers (2 papers)

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13 pages, 2453 KiB  
Article
Denovo-GCN: De Novo Peptide Sequencing by Graph Convolutional Neural Networks
by Ruitao Wu, Xiang Zhang, Runtao Wang and Haipeng Wang
Appl. Sci. 2023, 13(7), 4604; https://doi.org/10.3390/app13074604 - 5 Apr 2023
Cited by 4 | Viewed by 3160
Abstract
The de novo peptide-sequencing method can be used to directly infer the peptide sequence from a tandem mass spectrum. It has the advantage of not relying on protein databases and plays a key role in the determination of the protein sequences of unknown [...] Read more.
The de novo peptide-sequencing method can be used to directly infer the peptide sequence from a tandem mass spectrum. It has the advantage of not relying on protein databases and plays a key role in the determination of the protein sequences of unknown species, monoclonal antibodies, and cancer neoantigens. In this paper, we propose a method based on graph convolutional neural networks and convolutional neural networks, Denovo-GCN, for de novo peptide sequencing. We constructed an undirected graph based on the mass difference between the spectral peaks in a tandem mass spectrum. The features of the nodes on the spectrum graph, which represent the spectral peaks, were the matching information of the peptide sequence and the mass spectrum. Next, the Denovo-GCN used CNN to extract the features of the nodes. The correlation between the nodes was represented by an adjacency matrix, which aggregated the features of neighboring nodes. Denovo-GCN provides a complete end-to-end training and prediction framework to sequence patterns of peptides. Our experiments on various data sets from different species show that Denovo-GCN outperforms DeepNovo with a relative improvement of 13.7–25.5% in terms of the peptide-level recall. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Nanoscience, Engineering and Biomedical Research)
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13 pages, 1709 KiB  
Article
Inverse Design of Distributed Bragg Reflectors Using Deep Learning
by Sarah Head and Mehdi Keshavarz Hedayati
Appl. Sci. 2022, 12(10), 4877; https://doi.org/10.3390/app12104877 - 11 May 2022
Cited by 5 | Viewed by 3066
Abstract
Distributed Bragg Reflectors are optical structures capable of manipulating light behaviour, which are formed by stacking layers of thin-film materials. The inverse design of such structures is desirable, but not straightforward using conventional numerical methods. This study explores the application of Deep Learning [...] Read more.
Distributed Bragg Reflectors are optical structures capable of manipulating light behaviour, which are formed by stacking layers of thin-film materials. The inverse design of such structures is desirable, but not straightforward using conventional numerical methods. This study explores the application of Deep Learning to the design of a six-layer system, through the implementation of a Tandem Neural Network. The challenge is split into three sections: the generation of training data using the Transfer Matrix method, the design of a Simulation Neural Network (SNN) which maps structural geometry to spectral output, and finally an Inverse Design Neural Network (IDNN) which predicts the geometry required to produce target spectra. The latter enables the designer to develop custom multilayer systems with desired reflection properties. The SNN achieved an average accuracy of 97% across the dataset, with the IDNN achieving 94%. By using this inverse design method, custom-made reflectors can be manufactured in milliseconds, significantly reducing the cost of generating photonic devices and thin-film optics. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Nanoscience, Engineering and Biomedical Research)
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