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: closed (20 January 2024) | Viewed by 15131

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Guest Editor
Department of Mechanical Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: computational materials science; functional nanomaterials; materials informatics
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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

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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 2900 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

Related Special Issue

Published Papers (7 papers)

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Research

12 pages, 3841 KiB  
Article
Second Harmonic Generation in Janus Transition Metal Chalcogenide Oxide Monolayers: A First-Principles Investigation
by Peng Su, Han Ye, Naizhang Sun, Shining Liu and Hu Zhang
Nanomaterials 2023, 13(14), 2150; https://doi.org/10.3390/nano13142150 - 24 Jul 2023
Viewed by 1113
Abstract
Due to the unique optical responses induced by vertical atomic asymmetry inside a monolayer, two-dimensional Janus structures have been conceived as promising building blocks for nanoscale optical devices. In this paper, second harmonic generation (SHG) in Janus transition metal chalcogenide oxide monolayers is [...] Read more.
Due to the unique optical responses induced by vertical atomic asymmetry inside a monolayer, two-dimensional Janus structures have been conceived as promising building blocks for nanoscale optical devices. In this paper, second harmonic generation (SHG) in Janus transition metal chalcogenide oxide monolayers is systematically investigated by the first-principles calculations. Second-order nonlinear susceptibilities are theoretically determined for Janus MXO (M = Mo/W, X = S/Se/Te) monolayers. The calculated values are comparable in magnitude with Janus MoSSe monolayer. X-M-O symmetry breaking leads to non-zero components in vertical direction, compared with the non-Janus structure. Focusing on the SHG induced by incident light at 1064 nm, polarization-dependent responses of six Janus MXO monolayers are demonstrated. The symmetry of p-polarization changes from six-fold to three-fold with acute incidence angle. Moreover, the effects of biaxial strain on band structures and SHG are further investigated, taking MoSO as an exemplary case. We expect these results to bring in recipes for designing nonlinear optical devices based on Janus transition metal chalcogenide oxide monolayers. Full article
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17 pages, 11379 KiB  
Article
Unlocking the Power of Artificial Intelligence: Accurate Zeta Potential Prediction Using Machine Learning
by Rizwan Muneer, Muhammad Rehan Hashmet, Peyman Pourafshary and Mariam Shakeel
Nanomaterials 2023, 13(7), 1209; https://doi.org/10.3390/nano13071209 - 29 Mar 2023
Cited by 2 | Viewed by 1943
Abstract
Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes [...] Read more.
Nanoparticles have gained significance in modern science due to their unique characteristics and diverse applications in various fields. Zeta potential is critical in assessing the stability of nanofluids and colloidal systems but measuring it can be time-consuming and challenging. The current research proposes the use of cutting-edge machine learning techniques, including multiple regression analyses (MRAs), support vector machines (SVM), and artificial neural networks (ANNs), to simulate the zeta potential of silica nanofluids and colloidal systems, while accounting for affecting parameters such as nanoparticle size, concentration, pH, temperature, brine salinity, monovalent ion type, and the presence of sand, limestone, or nano-sized fine particles. Zeta potential data from different literature sources were used to develop and train the models using machine learning techniques. Performance indicators were employed to evaluate the models’ predictive capabilities. The correlation coefficient (r) for the ANN, SVM, and MRA models was found to be 0.982, 0.997, and 0.68, respectively. The mean absolute percentage error for the ANN model was 5%, whereas, for the MRA and SVM models, it was greater than 25%. ANN models were more accurate than SVM and MRA models at predicting zeta potential, and the trained ANN model achieved an accuracy of over 97% in zeta potential predictions. ANN models are more accurate and faster at predicting zeta potential than conventional methods. The model developed in this research is the first ever to predict the zeta potential of silica nanofluids, dispersed kaolinite, sand–brine system, and coal dispersions considering several influencing parameters. This approach eliminates the need for time-consuming experimentation and provides a highly accurate and rapid prediction method with broad applications across different fields. Full article
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11 pages, 3919 KiB  
Article
Molecular Dynamics Model to Explore the Initial Stages of Anion Exchange involving Layered Double Hydroxide Particles
by Gerard Novell Leruth, Alena Kuznetsova, João Tedim, José R. B. Gomes and Tiago L. P. Galvão
Nanomaterials 2022, 12(22), 4039; https://doi.org/10.3390/nano12224039 - 17 Nov 2022
Cited by 2 | Viewed by 1472
Abstract
A classical molecular dynamics (MD) model of fully unconstrained layered double hydroxide (LDH) particles in aqueous NaCl solution was developed to explore the initial stages of the anion exchange process, a key feature of LDHs for their application in different fields. In particular, [...] Read more.
A classical molecular dynamics (MD) model of fully unconstrained layered double hydroxide (LDH) particles in aqueous NaCl solution was developed to explore the initial stages of the anion exchange process, a key feature of LDHs for their application in different fields. In particular, this study focuses on the active corrosion protection mechanism, where LDHs are able to entrap aggressive species from the solution while releasing fewer corrosive species or even corrosion inhibitors. With this purpose in mind, it was explored the release kinetics of the delivery of nitrate and 2-mercaptobenzothiazole (MBT, a typical corrosion inhibitor) from layered double hydroxide particles triggered by the presence of aggressive chloride anions in solution. It was shown that the delamination of the cationic layers occurs during the anion exchange process, which is especially evident in the case of MBT. Full article
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9 pages, 5223 KiB  
Communication
Investigations on Grating-Enhanced Waveguides for Wide-Angle Light Couplings
by Yitong Gu, Ning Wang, Haorui Shang, Fei Yu and Lili Hu
Nanomaterials 2022, 12(22), 3991; https://doi.org/10.3390/nano12223991 - 12 Nov 2022
Cited by 1 | Viewed by 1391
Abstract
As a universal physical scheme, effective light couplings to waveguides favor numerous applications. However, the low coupling efficiency at wide angles prohibits this fundamental functionality and thus lowers the performance levels of photonic systems. As previously found, the transmission gratings patterned on waveguide [...] Read more.
As a universal physical scheme, effective light couplings to waveguides favor numerous applications. However, the low coupling efficiency at wide angles prohibits this fundamental functionality and thus lowers the performance levels of photonic systems. As previously found, the transmission gratings patterned on waveguide facets could significantly improve the large-angle-inputted efficiency to the order of 101. Here, we continue this study with a focus on a common scenario, i.e., a grating-modified waveguide excited by the Gaussian beam. A simplified 2D theoretical model is firstly introduced, proving that the efficiency lineshape could be well flattened by elaborately arranged diffractive gratings. For demonstration, subsequent explorations for proper grating geometries were conducted, and four structural configurations were selected for later full-wave numerical simulations. The last comparison studies showcase that the analytical method approximates the finite element method-based modelings. Both methods highlight grating-empowered coupling efficiencies, being 2.5 bigger than the counterparts of the previously reported seven-ring structure. All in all, our research provides instructions to simulate grating effects on the waveguide’s light-gathering abilities. Together with algorithm-designed coupling structures, it would be of great interest to further benefit real applications, such as bioanalytical instrumentation and quantum photon probes. Full article
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11 pages, 1830 KiB  
Article
The Design of Aluminum-Matrix Composites Reinforced with AlCoCrFeNi High-Entropy Alloy Nanoparticles by First-Principles Studies on the Properties of Interfaces
by Yu Liu and Guangping Zheng
Nanomaterials 2022, 12(13), 2157; https://doi.org/10.3390/nano12132157 - 23 Jun 2022
Cited by 2 | Viewed by 1639
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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13 pages, 11078 KiB  
Article
Nanoparticle Detection on SEM Images Using a Neural Network and Semi-Synthetic Training Data
by Jorge David López Gutiérrez, Itzel Maria Abundez Barrera and Nayely Torres Gómez
Nanomaterials 2022, 12(11), 1818; https://doi.org/10.3390/nano12111818 - 26 May 2022
Cited by 1 | Viewed by 2746
Abstract
Processing images represents a necessary step in the process of analysing the information gathered about nanoparticles after characteristic material samples have been scanned with electron microscopy, which often requires the use of image processing techniques or general purpose image manipulation software to carry [...] Read more.
Processing images represents a necessary step in the process of analysing the information gathered about nanoparticles after characteristic material samples have been scanned with electron microscopy, which often requires the use of image processing techniques or general purpose image manipulation software to carry out tasks such as nanoparticle detection and measurement. In recent years, the use of networks has been successfully implemented to detect and classify electron microscopy images as well as the objects within them. In this work, we present four detection models using two versions of the YOLO neural network architectures trained to detect cubical and quasi-spherical particles in SEM images; the training datasets are a mixture of real images and synthetic ones generated by a semi-arbitrary method. The resulting models were capable of detecting nanoparticles in images different than the ones used for training and identifying them in some cases as the close proximity between nanoparticles proved a challenge for the neural networks in most situations. Full article
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15 pages, 8732 KiB  
Article
Smart and Rapid Design of Nanophotonic Structures by an Adaptive and Regularized Deep Neural Network
by Renjie Li, Xiaozhe Gu, Yuanwen Shen, Ke Li, Zhen Li and Zhaoyu Zhang
Nanomaterials 2022, 12(8), 1372; https://doi.org/10.3390/nano12081372 - 16 Apr 2022
Cited by 6 | Viewed by 3906
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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