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).
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 [-5, +20] QCL dataset using 50 neurons, ReLU activation function, solver Adam, 0.001 learning rate, over 50 epochs, and is saved to be used in the prediction of figure of merit values for QCL designs (.mat file).
A dataset of 2400 quantum cascade structures at 15 electric field iterations, for a total of 36000 unique designs. The structures are generated by randomly altering a starting 10-layer design of alternating Al0.48In0.52As barrier material and In0.53Ga0.47As well material, with layer thickness sequence of 9/57/11/54/12/45/25/34/14/33 Angstroms (starting with well material). The random tolerance range is from -5 to +20 Angstroms in 5 Angstrom increments. The laser transition Figure of Merit, among other quantities of interest, is identified for each design using a method found in:
A. C. Hernandez, M. Lyu and C. F. Gmachl, "Generating Quantum Cascade Laser Datasets for Applications in Machine Learning," 2022 IEEE Photonics Society Summer Topicals Meeting Series (SUM), 2022, pp. 1-2, doi: 10.1109/SUM53465.2022.9858281
A code to identify the laser transition for a quantum cascade laser design based on the figure of merit. Variables such as the number of layers, and layer thicknesses, as well the applied electric field, materials composition, number of period repetitions, and layer tolerance ranges to generate random designs are specified. A folder containing a .csv file with all electronic state-pair transitions collected, a .png file of the bandstructure and the laser transition chosen (in red), for all electric field iterations, and a summary .csv file of all these laser transitions for a structure at each electric field is generated by the code. To use, first install ErwinJr2 on your computer. Then locate the "ErwinJr2" folder and copy these 6 files into that directory, overwriting the previous five files (Material.py, QCLayers.py, QCPlotter.py, QuantumTab.py, rFittings.py). Lastly, run the "acej-qcl-layer_10-lwrandom-v23.py" script using Python.
The "summary-fomstar-3lu-eVmiddle-19.csv" file is generated after running the laser transition code, with all of the data collected for one structure at many electric fields. Running the script various times will generate random structures with the same electric field range. Joining these "summary" .csv files makes a QCL dataset.
