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Author(s):: Correa Hernandez, Andres; Gmachl, Claire F.
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 [-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).
Type:: Software
Issue Date:: 20 March 2024

Author(s):: Correa Hernandez, Andres; Gmachl, Claire F.
Abstract:: 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
Type:: Dataset
Issue Date:: 20 March 2024

Author(s):: Correa Hernandez, Andres; Gmachl, Claire F
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).
Type:: Software
Issue Date:: 16 February 2024

Author(s):: Correa Hernandez, Andres; Gmachl, Claire F
Abstract:: 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.
Type:: Software
Issue Date:: 16 February 2024

Author(s):: Hernandez, Andres Correa; Gmachl, Claire F.
Abstract:: This dataset contains 1800 quantum cascade (QC) structures generated by randomly modifying an initial 10-layer design in the tolerance range of -2 to +3 Angstroms at an applied electric field range of 0 to 150 kV/cm (in 10 kV/cm increments). One structure at one electric field is one design, thus there are 27000 unique designs, represented as a row in the dataset. The layer thicknesses (in angstroms) and the electric field are inputs which get evaluated using a Schrödinger solver, ErwinJr2, to identify the laser transition Figure of Merit (fom*), among other reported outputs.
Type:: Dataset
Issue Date:: December 2023