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Nanomaterials
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TERA-MIR Photonics, Materials and Devices
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TERA-MIR Photonics, Materials and Devices

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Nanophotonics Materials and Devices".

Deadline for manuscript submissions: closed (20 May 2026) | Viewed by 396

Special Issue Editor

Prof. Dr. Mauro Fernandes Pereira

E-Mail Website
Guest Editor
1. Department of Physics, Khalifa University, Abu Dhabi 127788, United Arab Emirates
2. Institute of Physics CAS, Na Slovance 1999/2, 182 21 Prague, Czech Republic
Interests: terahertz and mid-infrared radiation; quantum transport and optics; many-body effects; nonequilibrium Green's functions; bandstructure engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The primary objective of this Special Issue is to highlight recent advances in materials, concepts, and device architectures that enable the generation and detection of radiation across the Terahertz (0.1–10 THz) and Mid-Infrared (10–100 THz) spectral ranges. These regions bridge the gap between electronics and photonics, offering unique opportunities for imaging, spectroscopy, and sensing. The issue will feature developments in semiconductor, superconductor, and metamaterial platforms, as well as innovative laser-based sources and detectors. Emphasis will be placed on photonic integration, new materials, and system-level approaches that enhance performance and scalability. In addition to fundamental science, contributions addressing emerging applications are strongly encouraged—from environmental monitoring and chemical sensing to biomedical diagnostics and food safety. By bringing together experts from diverse disciplines, this Special Issue aims to provide a comprehensive overview of the state of the art and outline the technological developments that will shape the future of TERA–MIR photonics.

Prof. Dr. Mauro Fernandes Pereira
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 250 words) can be sent to the Editorial Office for assessment.

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

  • terahertz
  • mid-infrared
  • photonics
  • lasers
  • detectors
  • devices
  • THz materials
  • THz devices
  • medical diagnostics
 
 

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Published Papers (1 paper)

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Research

21 pages, 9662 KB  
Article
Machine Learning Models for Predicting Key Performance Characteristics of High-Temperature THz Quantum Cascade Lasers
by Mihailo Stojković, Novak Stanojević, Aleksandar Milićević, Nikola Vuković, Dušan Topalović, Milan Ignjatović, Aleksandar Demić, Dragan Indjin and Jelena Radovanović
Nanomaterials 2026, 16(11), 651; https://doi.org/10.3390/nano16110651 - 22 May 2026
Viewed by 150
Abstract
In this work, we applied Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) to predict key performance characteristics of quantum cascade lasers (QCLs), including material gain, current density, and emission frequency. By developing a machine learning-based surrogate modeling framework [...] Read more.
In this work, we applied Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN) to predict key performance characteristics of quantum cascade lasers (QCLs), including material gain, current density, and emission frequency. By developing a machine learning-based surrogate modeling framework that replaces computationally expensive simulations of QCLs, we enable orders-of-magnitude-faster evaluation and optimization of a high-dimensional configuration space. The training dataset was generated using a numerical simulator based on the density-matrix transport model. By combining physics simulations with machine learning, we achieved reliable predictions of device characteristics, with standardized RMSE values ranging from 0.21 to 0.55 for RF, 0.16 to 0.51 for XGBoost, and 0.04 to 0.22 for the ANN model, demonstrating the superior predictive performance of the ANN across all investigated performance characteristics. The ANN was subsequently used to analyze the full configuration space defined by possible layer thicknesses and electric fields. Approximately 44 million configurations were evaluated in about five minutes, achieving a speedup of approximately 90,000 times over the numerical simulator for a single configuration. This approach allowed the identification of designs with improved material gain and facilitated the efficient optimization of key parameters while maintaining high prediction reliability. Full article
(This article belongs to the Special Issue TERA-MIR Photonics, Materials and Devices)
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