Nanomanufacturing Empowered with Artificial Intelligence

A special issue of Nanomanufacturing (ISSN 2673-687X).

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 1109

Special Issue Editors


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Guest Editor
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA
Interests: metamaterials; machine learning; nanophotonics; plasmonics; optics

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Guest Editor
School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: scalable nanomanufacturing: lithography and imaging; optical and magnetic data storage; nanoscale energy conversion, transfer and storage for alternative energy

Special Issue Information

Dear Colleagues,

Over the past decades, artificial intelligence has become the hottest research topic that is gradually impacting almost all aspects of human life.

As Moore’s law indicates, the number of microchip transistors roughly doubles every two years. Throughout the past fifty years, technique improvements in nanomanufacturing have consistently added power to computers, which nurtures the growth of artificial intelligence (AI).

Eventually, this year we witnessed the boom of ChatGPT. Such AI techniques will significantly impact science and technology and can potentially update our technology to a new level. The era of AI is coming, which may lead to the next Industrial Revolution.

As artificial intelligence keeps growing every day, we think it is of great importance to discuss the impacts of AI on its soil, namely, nanomanufacturing. This Special Issue is designed to cover topics about nanomanufacturing techniques empowered with AI-related techniques.

Topics include but are not limited to the following:

  • Machine learning/deep learning methods applied in nanomanufacturing;
  • Design of machine learning/deep learning algorithms for multiscale manufacturing;
  • Top-down and bottom-up nanomanufacturing techniques optimized with artificial intelligence;
  • Nanodevices designed and fabricated for AI applications;
  • Nanoscale storage systems for big data and high-performance computing platforms;
  • Discovery of nanomaterial properties with machine-learning methods.

Dr. Feng Cheng
Dr. Liang Pan
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 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. Nanomanufacturing is an international peer-reviewed open access quarterly 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 1000 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 (1 paper)

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26 pages, 3681 KiB  
Article
New Polymers In Silico Generation and Properties Prediction
by Andrey A. Knizhnik, Pavel V. Komarov, Boris V. Potapkin, Denis B. Shirabaykin, Alexander S. Sinitsa and Sergey V. Trepalin
Nanomanufacturing 2024, 4(1), 1-26; https://doi.org/10.3390/nanomanufacturing4010001 - 19 Dec 2023
Viewed by 818
Abstract
We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for [...] Read more.
We present a theoretical approach for the in silico generation of new polymer structures for the systematic search for new materials with advanced properties. It is based on Bicerano’s Regression Model (RM), which uses the structure of the smallest repeating unit (SRU) for fast and adequate prediction of polymer properties. We have developed the programs (a) GenStruc, for generating the new polymer SRUs using the enumeration and Monte Carlo algorithms, and (b) PolyPred, for predicting properties for a given input polymer as well as for multiple structures stored in the database files. The structure database from the original Bicerano publication is used to create databases of backbones and pendant groups. A database of 5,142,153 unique SRUs is generated using the scaffold-based combinatorial method. We show that using only known backbones of the polymer SRU and varying the pendant groups can significantly improve the predicted extreme values of polymer properties. Analysis of the obtained results for the dielectric constant and refractive index shows that the values of the dielectric constant are higher for polyhydrazides than for polyhydroxylamines. The high value predicted for the refractive index of polythiophene and its derivatives is in agreement with the experimental data. Full article
(This article belongs to the Special Issue Nanomanufacturing Empowered with Artificial Intelligence)
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