Issue date: 16 February 2024
Correa Hernandez, Andres & Gmachl, Claire F. (2024). MLP neural network trained on the QCL [-2, +3] Å dataset [Data set]. Princeton University. https://doi.org/10.34770/e034-4670
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Abstract: This item contains two files. A multi-layer perceptron (MLP) neural network is built using the MATLAB Deep Network Designer (.m file). It imports a quantum cascade laser (QCL) dataset and splits it into 70% training, 15% validation, and 15% testing subsets. The network consists of an input layer, three hidden layers (each having a normalization and activation layer), and a regression output layer. All of the layers are fully connected, and the root-mean-square error (RMSE) is used to evaluate the accuracy of the network. An algorithm is trained on the [-2, +3] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 150 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
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Description: Details on building machine learning algorithms for QCL design can be found in [1] and the [-2, +3] Å QCL dataset can be found in [2]. References: [1] A. C. Hernandez and C. F. Gmachl, “Application of Machine Learning to Quantum Cascade Laser Design,” in 2023 57th Annual Conference on Information Sciences and Systems, CISS 2023, 2023. doi: 10.1109/CISS56502.2023.10089756. [2] A. Correa Hernandez, C. F. Gmachl, and M. Lyu, “QCL Dataset, 10 Layer Structure, Tolerance [-2, +3] A, Electric Field [0,10,150] kV/cm.” Princeton University, Princeton, 2023. doi: https://doi.org/10.34770/r7nr-ee50.
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