@inproceedings{efc2adc616e24bdc99edf86e5c680dde,
title = "Machine Learning for Quantum Cascade Laser Design and Optimization",
abstract = "A machine learning framework is used to predict the laser performance of 109 quantum cascade laser designs in 8 hours. The algorithm demonstrates how to optimize the layer structure, yielding a 2-fold increase in performance.",
keywords = "Electric fields, Electro-optical waveguides, Laser transitions, Lasers and electrooptics, Machine learning, Machine learning algorithms, Microcomputers, Optimization, Prediction algorithms, Quantum cascade lasers",
author = "Hernandez, {Andres Correa} and Gmachl, {Claire F.}",
note = "Publisher Copyright: {\textcopyright} Optica Publishing Group 2024 {\textcopyright}2024TheAuthor(s); 2024 Conference on Lasers and Electro-Optics, CLEO 2024 ; Conference date: 07-05-2024 Through 10-05-2024",
year = "2024",
doi = "10.1364/cleo_si.2024.sw3h.3",
language = "English (US)",
series = "2024 Conference on Lasers and Electro-Optics, CLEO 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 Conference on Lasers and Electro-Optics, CLEO 2024",
address = "United States",
}