Special Issue "Design of Nanomaterials by Computer Simulation and Artificial Intelligence Approaches"

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Theory and Simulation of Nanostructures".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 987

Special Issue Editor

Dr. Guang-Ping Zheng
E-Mail Website
Guest Editor
Department of Mechanical Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: computational materials science; functional nanomaterials; materials informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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
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. 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.

Keywords

  • 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

Published Papers (2 papers)

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Research

Article
The Design of Aluminum-Matrix Composites Reinforced with AlCoCrFeNi High-Entropy Alloy Nanoparticles by First-Principles Studies on the Properties of Interfaces
Nanomaterials 2022, 12(13), 2157; https://doi.org/10.3390/nano12132157 - 23 Jun 2022
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Abstract
The present work reports the interfacial behaviors and mechanical properties of AlCoCrFeNi high-entropy alloy (HEA) reinforced aluminum matrix composites (AMCs) based on first-principles calculations. It is found the stability of HEA-reinforced AMCs is strongly dependent on the local chemical compositions in the interfacial [...] Read more.
The present work reports the interfacial behaviors and mechanical properties of AlCoCrFeNi high-entropy alloy (HEA) reinforced aluminum matrix composites (AMCs) based on first-principles calculations. It is found the stability of HEA-reinforced AMCs is strongly dependent on the local chemical compositions in the interfacial regions, i.e., those regions containing more Ni atoms (>25%) or fewer Al atoms (<20%) render more stable interfaces in the HEA-reinforced AMCs. It is calculated that the interfacial energy of Al(001)/Al20Co19Cr19Fe19Ni19(001) interfaces varies from −0.242 eV/Å2 to −0.192 eV/Å2, suggesting that the formation of interfaces at (100) atomic plane is energetically favorable. For those constituent alloy elements presented at the interfaces, Ni could stabilize the interface whereas Al tends to deteriorate the stability of interface. It is determined that although the HEA-reinforced AMCs have less yield strength compared to aluminum, their Young’s modulus is enhanced from 69 GPa for pure Al to 134 GPa. Meanwhile, the meaningful plasticity under tension could also be improved, which are related to the chemical compositions at the interfaces. The results presented in this work could facilitate the designs of compositions and interfacial behaviors of HEA-reinforced AMCs for structural applications. Full article
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Article
Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network
Nanomaterials 2022, 12(8), 1372; https://doi.org/10.3390/nano12081372 - 16 Apr 2022
Viewed by 618
Abstract
The design of nanophotonic structures based on deep learning is emerging rapidly in the research community. Design methods using Deep Neural Networks (DNN) are outperforming conventional physics-based simulations performed iteratively by human experts. Here, a self-adaptive and regularized DNN based on Convolutional Neural [...] Read more.
The design of nanophotonic structures based on deep learning is emerging rapidly in the research community. Design methods using Deep Neural Networks (DNN) are outperforming conventional physics-based simulations performed iteratively by human experts. Here, a self-adaptive and regularized DNN based on Convolutional Neural Networks (CNNs) for the smart and fast characterization of nanophotonic structures in high-dimensional design parameter space is presented. This proposed CNN model, named LRS-RCNN, utilizes dynamic learning rate scheduling and L2 regularization techniques to overcome overfitting and speed up training convergence and is shown to surpass the performance of all previous algorithms, with the exception of two metrics where it achieves a comparable level relative to prior works. We applied the model to two challenging types of photonic structures: 2D photonic crystals (e.g., L3 nanocavity) and 1D photonic crystals (e.g., nanobeam) and results show that LRS-RCNN achieves record-high prediction accuracies, strong generalizibility, and substantially faster convergence speed compared to prior works. Although still a proof-of-concept model, the proposed smart LRS-RCNN has been proven to greatly accelerate the design of photonic crystal structures as a state-of-the-art predictor for both Q-factor and V. It can also be modified and generalized to predict any type of optical properties for designing a wide range of different nanophotonic structures. The complete dataset and code will be released to aid the development of related research endeavors. Full article
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