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Recent Progress in Advanced Computing Techniques for Molecules and Nanomaterials

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Nanoscience".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 2943

Special Issue Editor

Physics Department, Gachon University, Seongnam-si, Gyeonggi-do, Republic of Korea
Interests: nanomaterials; machine learning; artificial intelligence; energy storage; molecular dynamics; supercapacitor; electrochemical capacitors; energy materials; fuel cells
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Special Issue Information

Dear Colleagues,

Recent developments in advanced computing techniques have significantly contributed to the design and property prediction of materials at the molecular level and the nanoscale. Traditional quantum mechanical methods, such as Density Functional Theory (DFT), have been widely used to accurately compute the electronic structures of molecules and nanomaterials. However, the introduction of high-performance computing and quantum computing techniques has dramatically increased the speed and precision of such research. For example, machine learning (ML) and artificial intelligence (AI)-based methodologies play a key role in efficiently modeling complex physical and chemical interactions by processing large datasets. In particular, studies combining molecular dynamics (MD) simulations with hybrid modeling techniques are contributing to the in-depth analysis of the mechanisms underlying nanomaterials. Furthermore, quantum algorithms, especially the Variational Quantum Eigensolver (VQE), have emerged as powerful tools for solving many-body problems that are challenging for classical computations. In nanomaterials research, advanced computing techniques have been applied to predict the electrochemical and optical properties of materials such as quantum dots, nanotubes, and graphene and its derivatives. This has led to groundbreaking developments in applications ranging from energy storage and catalysis to sensor technologies. This Special Issue highlights the progress and applications of advanced computational technologies in the field of computational chemistry and nanomaterials, offering insights into future research directions.

Dr. Joonho Bae
Guest Editor

Manuscript Submission Information

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Keywords

  • nanomaterials
  • machine learning
  • artificial intelligence
  • computing techniques
  • modeling
  • energy storage
  • catalysis

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Published Papers (1 paper)

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Review

27 pages, 11141 KiB  
Review
A Review of Bandgap Engineering and Prediction in 2D Material Heterostructures: A DFT Perspective
by Yoonju Oh, Seunghyun Song and Joonho Bae
Int. J. Mol. Sci. 2024, 25(23), 13104; https://doi.org/10.3390/ijms252313104 - 6 Dec 2024
Cited by 3 | Viewed by 2534
Abstract
The advent of two-dimensional (2D) materials and their capacity to form van der Waals (vdW) heterostructures has revolutionized numerous scientific fields, including electronics, optoelectronics, and energy storage. This paper presents a comprehensive investigation of bandgap engineering and band structure prediction in 2D vdW [...] Read more.
The advent of two-dimensional (2D) materials and their capacity to form van der Waals (vdW) heterostructures has revolutionized numerous scientific fields, including electronics, optoelectronics, and energy storage. This paper presents a comprehensive investigation of bandgap engineering and band structure prediction in 2D vdW heterostructures utilizing density functional theory (DFT). By combining various 2D materials, such as graphene, hexagonal boron nitride (h-BN), transition metal dichalcogenides, and blue phosphorus, these heterostructures exhibit tailored properties that surpass those of individual components. Bandgap engineering represents an effective approach to addressing the limitations inherent in material properties, thereby providing enhanced functionalities for a range of applications, including transistors, photodetectors, and solar cells. Furthermore, this study discusses the current limitations and challenges associated with bandgap engineering in 2D heterostructures and highlights future prospects aimed at unlocking their full potential for advanced technological applications. Full article
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