Advances in Theoretical and Computational Physics of Low-Dimensional Materials

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

Deadline for manuscript submissions: 26 December 2025 | Viewed by 311

Special Issue Editor

Institute for quantum science and technology, Shanghai University, Shanghai 200444, China
Interests: 2D Materials; topological materials; magnetism

Special Issue Information

Dear Colleagues,

Recent breakthroughs in nanotechnology have intensified scientific interest in low-dimensional materials, from atomically thin semiconductors to carbon nanostructures. These systems exhibit remarkable properties distinct from their bulk counterparts, yet their rational development requires a precise understanding of complex quantum interactions and structural sensitivities. Theoretical modeling and computational simulations have emerged as critical tools to address these challenges, enabling researchers to decode structure–property relationships at previously inaccessible resolutions. This Special Issue aims to focus on advancing methodologies that synergize physical principles with numerical techniques to both interpret experimental observations and predict novel material functionalities.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Theoretical and simulation approaches for studying low-dimensional materials;
  • Prediction of low-dimensional material structure and properties;
  • Exploration of low-dimensional material fundamentals, behaviors, degradation, and properties;
  • Integrative studies combining computational findings with experimental validation to examine the behavior of low-dimensional materials;
  • Design of low-dimensional materials using machine learning and artificial intelligence.

Dr. Jie Li
Guest Editor

Manuscript Submission Information

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Keywords

  • low-dimensional materials
  • structure
  • simulation
  • theoretical
  • computational

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Published Papers (1 paper)

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Research

13 pages, 2199 KiB  
Article
Non-Invasive Composition Identification in Organic Solar Cells via Deep Learning
by Yi-Hsun Chang, You-Lun Zhang, Cheng-Hao Cheng, Shu-Han Wu, Cheng-Han Li, Su-Yu Liao, Zi-Chun Tseng, Ming-Yi Lin and Chun-Ying Huang
Nanomaterials 2025, 15(14), 1112; https://doi.org/10.3390/nano15141112 - 17 Jul 2025
Viewed by 201
Abstract
Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To [...] Read more.
Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. Full article
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